Data is the cornerstone of deep learning. This paper reveals that the recently developed Diffusion Model is a scalable data engine for object detection. Existing methods for scaling up detection-oriented data often require manual collection or generative models to obtain target images, followed by data augmentation and labeling to produce training pairs, which are costly, complex, or lacking diversity. To address these issues, we presentDiffusionEngine (DE), a data scaling-up engine that provides high-quality detection-oriented training pairs in a single stage. DE consists of a pre-trained diffusion model and an effective Detection-Adapter, contributing to generating scalable, diverse and generalizable detection data in a plug-and-play manner. Detection-Adapter is learned to align the implicit semantic and location knowledge in off-the-shelf diffusion models with detection-aware signals to make better bounding-box predictions. Additionally, we contribute two datasets, i.e., COCO-DE and VOC-DE, to scale up existing detection benchmarks for facilitating follow-up research. Extensive experiments demonstrate that data scaling-up via DE can achieve significant improvements in diverse scenarios, such as various detection algorithms, self-supervised pre-training, data-sparse, label-scarce, cross-domain, and semi-supervised learning. For example, when using DE with a DINO-based adapter to scale up data, mAP is improved by 3.1% on COCO, 7.6% on VOC, and 11.5% on Clipart.  ( 2 min )

Researchers are solving the challenges of spatial-temporal prediction by combining Federated Learning (FL) and graph models with respect to the constrain of privacy and security. In order to make better use of the power of graph model, some researchs also combine split learning(SL). However, there are still several issues left unattended: 1) Clients might not be able to access the server during inference phase; 2) The graph of clients designed manually in the server model may not reveal the proper relationship between clients. This paper proposes a new GNN-oriented split federated learning method, named node {\bfseries M}asking and {\bfseries M}ulti-granularity {\bfseries M}essage passing-based Federated Graph Model (M$^3$FGM) for the above issues. For the first issue, the server model of M$^3$FGM employs a MaskNode layer to simulate the case of clients being offline. We also redesign the decoder of the client model using a dual-sub-decoders structure so that each client model can use its local data to predict independently when offline. As for the second issue, a new GNN layer named Multi-Granularity Message Passing (MGMP) layer enables each client node to perceive global and local information. We conducted extensive experiments in two different scenarios on two real traffic datasets. Results show that M$^3$FGM outperforms the baselines and variant models, achieves the best results in both datasets and scenarios.  ( 3 min )

Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples.  ( 2 min )

There has been much recent progress in forecasting the next observation of a linear dynamical system (LDS), which is known as the improper learning, as well as in the estimation of its system matrices, which is known as the proper learning of LDS. We present an approach to proper learning of LDS, which in spite of the non-convexity of the problem, guarantees global convergence of numerical solutions to a least-squares estimator. We present promising computational results.  ( 2 min )

Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node embeddings for various graph inference tasks, but training them can raise privacy concerns. To address this, we propose using link local differential privacy over decentralized nodes, enabling collaboration with an untrusted server to train GNNs without revealing the existence of any link. Our approach spends the privacy budget separately on links and degrees of the graph for the server to better denoise the graph topology using Bayesian estimation, alleviating the negative impact of LDP on the accuracy of the trained GNNs. We bound the mean absolute error of the inferred link probabilities against the ground truth graph topology. We then propose two variants of our LDP mechanism complementing each other in different privacy settings, one of which estimates fewer links under lower privacy budgets to avoid false positive link estimates when the uncertainty is high, while the other utilizes more information and performs better given relatively higher privacy budgets. Furthermore, we propose a hybrid variant that combines both strategies and is able to perform better across different privacy budgets. Extensive experiments show that our approach outperforms existing methods in terms of accuracy under varying privacy budgets.  ( 3 min )

The atmosphere affects humans in a multitude of ways, from loss of life due to adverse weather effects to long-term social and economic impacts on societies. Computer simulations of atmospheric dynamics are, therefore, of great importance for the well-being of our and future generations. Here, we propose AtmoRep, a novel, task-independent stochastic computer model of atmospheric dynamics that can provide skillful results for a wide range of applications. AtmoRep uses large-scale representation learning from artificial intelligence to determine a general description of the highly complex, stochastic dynamics of the atmosphere from the best available estimate of the system's historical trajectory as constrained by observations. This is enabled by a novel self-supervised learning objective and a unique ensemble that samples from the stochastic model with a variability informed by the one in the historical record. The task-independent nature of AtmoRep enables skillful results for a diverse set of applications without specifically training for them and we demonstrate this for nowcasting, temporal interpolation, model correction, and counterfactuals. We also show that AtmoRep can be improved with additional data, for example radar observations, and that it can be extended to tasks such as downscaling. Our work establishes that large-scale neural networks can provide skillful, task-independent models of atmospheric dynamics. With this, they provide a novel means to make the large record of atmospheric observations accessible for applications and for scientific inquiry, complementing existing simulations based on first principles.  ( 3 min )

With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. However, most of the base clusterers (i.e., clustering algorithms) used in existing federated clustering algorithms need to specify the number of clusters in advance. These algorithms, therefore, are unable to deal with data whose distributions are unknown or continually changing. To tackle this problem, this paper proposes a privacy-preserving continual federated clustering algorithm. In the proposed algorithm, an adaptive resonance theory-based clustering algorithm capable of continual learning is used as a base clusterer. Therefore, the proposed algorithm inherits the ability of continual learning. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art federated clustering algorithms while realizing data privacy protection and continual learning ability. The source code is available at \url{https://github.com/Masuyama-lab/FCAC}.  ( 2 min )

As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In practice, new input data tend to come without target labels. Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions. We suggest a novel approach that models how explanation characteristics shift when affected by distribution shifts. We find that the modeling of explanation shifts can be a better indicator for detecting out-of-distribution model behaviour than state-of-the-art techniques. We analyze different types of distribution shifts using synthetic examples and real-world data sets. We provide an algorithmic method that allows us to inspect the interaction between data set features and learned models and compare them to the state-of-the-art. We release our methods in an open-source Python package, as well as the code used to reproduce our experiments.  ( 2 min )

While a key component to the success of deep learning is the availability of massive amounts of training data, medical image datasets are often limited in diversity and size. Transfer learning has the potential to bridge the gap between related yet different domains. For medical applications, however, it remains unclear whether it is more beneficial to pre-train on natural or medical images. We aim to shed light on this problem by comparing initialization on ImageNet and RadImageNet on seven medical classification tasks. Our work includes a replication study, which yields results contrary to previously published findings. In our experiments, ResNet50 models pre-trained on ImageNet tend to outperform those trained on RadImageNet. To gain further insights, we investigate the learned representations using Canonical Correlation Analysis (CCA) and compare the predictions of the different models. Our results indicate that, contrary to intuition, ImageNet and RadImageNet may converge to distinct intermediate representations, which appear to diverge further during fine-tuning. Despite these distinct representations, the predictions of the models remain similar. Our findings show that the similarity between networks before and after fine-tuning does not correlate with performance gains, suggesting that the advantages of transfer learning might not solely originate from the reuse of features in the early layers of a convolutional neural network.

This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification. We emphasize tools we deem particularly useful for a range of problems in this domain, such as the covering technique, the Hanson-Wright Inequality and the method of self-normalized martingales. We then employ these tools to give streamlined proofs of the performance of various least-squares based estimators for identifying the parameters in autoregressive models. We conclude by sketching out how the ideas presented herein can be extended to certain nonlinear identification problems.

The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate clustering results. A very popular measure is the Silhouette. We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its properties, provide two fast versions for the direct optimization, and discuss the use to choose the optimal number of clusters. We combine ideas from the original Silhouette with the well-known PAM algorithm and its latest improvements FasterPAM. One of the versions guarantees equal results to the original variant and provides a run speedup of $O(k^2)$. In experiments on real data with 30000 samples and $k$=100, we observed a 10464$\times$ speedup compared to the original PAMMEDSIL algorithm. Additionally, we provide a variant to choose the optimal number of clusters directly.

Typical technique in knowledge distillation (KD) is regularizing the learning of a limited capacity model (student) by pushing its responses to match a powerful model's (teacher). Albeit useful especially in the penultimate layer and beyond, its action on student's feature transform is rather implicit, limiting its practice in the intermediate layers. To explicitly embed the teacher's knowledge in feature transform, we propose a learnable KD layer for the student which improves KD with two distinct abilities: i) learning how to leverage the teacher's knowledge, enabling to discard nuisance information, and ii) feeding forward the transferred knowledge deeper. Thus, the student enjoys the teacher's knowledge during the inference besides training. Formally, we repurpose 1x1-BN-ReLU-1x1 convolution block to assign a semantic vector to each local region according to the template (supervised by the teacher) that the corresponding region of the student matches. To facilitate template learning in the intermediate layers, we propose a novel form of supervision based on the teacher's decisions. Through rigorous experimentation, we demonstrate the effectiveness of our approach on 3 popular classification benchmarks. Code is available at: https://github.com/adagorgun/letKD-framework

Multiagent systems aim to accomplish highly complex learning tasks through decentralised consensus seeking dynamics and their use has garnered a great deal of attention in the signal processing and computational intelligence societies. This article examines the behaviour of multiagent networked systems with nonlinear filtering/learning dynamics. To this end, a general formulation for the actions of an agent in multiagent networked systems is presented and conditions for achieving a cohesive learning behaviour is given. Importantly, application of the so derived framework in distributed and federated learning scenarios are presented.

The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4. However, many commercial conversational agent development tools are pipeline-based and have limitations in holding a human-like conversation. This paper investigates the capabilities of LLMs to enhance pipeline-based conversational agents during two phases: 1) in the design and development phase and 2) during operations. In 1) LLMs can aid in generating training data, extracting entities and synonyms, localization, and persona design. In 2) LLMs can assist in contextualization, intent classification to prevent conversational breakdown and handle out-of-scope questions, auto-correcting utterances, rephrasing responses, formulating disambiguation questions, summarization, and enabling closed question-answering capabilities. We conducted informal experiments with GPT-4 in the private banking domain to demonstrate the scenarios above with a practical example. Companies may be hesitant to replace their pipeline-based agents with LLMs entirely due to privacy concerns and the need for deep integration within their existing ecosystems. A hybrid approach in which LLMs' are integrated into the pipeline-based agents allows them to save time and costs of building and running agents by capitalizing on the capabilities of LLMs while retaining the integration and privacy safeguards of their existing systems.

In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? Information theory gives one answer: To maximize the information in the model, select the most likely objective function or whichever represents the error in the fewest bits. To evaluate different objectives, transform them into likelihood functions. As likelihoods, their relative magnitudes represent how much we should prefer one objective versus another, and the log of their magnitude represents the expected uncertainty of the model.

Decisions made by machine learning models may have lasting impacts over time, making long-term fairness a crucial consideration. It has been shown that when ignoring the long-term effect, naively imposing fairness criterion in static settings can actually exacerbate bias over time. To explicitly address biases in sequential decision-making, recent works formulate long-term fairness notions in Markov Decision Process (MDP) framework. They define the long-term bias to be the sum of static bias over each time step. However, we demonstrate that naively summing up the step-wise bias can cause a false sense of fairness since it fails to consider the importance difference of different time steps during transition. In this work, we introduce a long-term fairness notion called Equal Long-term Benefit Rate (ELBERT), which explicitly considers varying temporal importance and adapts static fairness principles to the sequential setting. Moreover, we show that the policy gradient of Long-term Benefit Rate can be analytically reduced to standard policy gradient. This makes standard policy optimization methods applicable for reducing the bias, leading to our proposed bias mitigation method ELBERT-PO. Experiments on three sequential decision making environments show that ELBERT-PO significantly reduces bias and maintains high utility. Code is available at https://github.com/Yuancheng-Xu/ELBERT.

Low Rank Decomposition (LRD) is a model compression technique applied to the weight tensors of deep learning models in order to reduce the number of trainable parameters and computational complexity. However, due to high number of new layers added to the architecture after applying LRD, it may not lead to a high training/inference acceleration if the decomposition ranks are not small enough. The issue is that using small ranks increases the risk of significant accuracy drop after decomposition. In this paper, we propose two techniques for accelerating low rank decomposed models without requiring to use small ranks for decomposition. These methods include rank optimization and sequential freezing of decomposed layers. We perform experiments on both convolutional and transformer-based models. Experiments show that these techniques can improve the model throughput up to 60% during training and 37% during inference when combined together while preserving the accuracy close to that of the original models

All fields of science depend on mathematical models. Occam's razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how deep learning can be powerfully leveraged to address Occam's razor. FixFit, our new method, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First, it provides a metric to quantify the original model's degree of complexity. Second, it allows for the unique fitting of data. Third, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In two use cases, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system, we apply FixFit to recover known composite parameters for the Kepler orbit model. To illustrate how the method can be applied to less well-established fields, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms.

Recent advances in the field of deep learning and impressive performance of deep neural networks (DNNs) for perception have resulted in an increased demand for their use in automated driving (AD) systems. The safety of such systems is of utmost importance and thus requires to consider the unique properties of DNNs. In order to achieve safety of AD systems with DNN-based perception components in a systematic and comprehensive approach, so-called safety concerns have been introduced as a suitable structuring element. On the one hand, the concept of safety concerns is -- by design -- well aligned to existing standards relevant for safety of AD systems such as ISO 21448 (SOTIF). On the other hand, it has already inspired several academic publications and upcoming standards on AI safety such as ISO PAS 8800. While the concept of safety concerns has been previously introduced, this paper extends and refines it, leveraging feedback from various domain and safety experts in the field. In particular, this paper introduces an additional categorization for a better understanding as well as enabling cross-functional teams to jointly address the concerns.

Large language models, particularly those akin to the rapidly progressing GPT series, are gaining traction for their expansive influence. While there is keen interest in their applicability within medical domains such as psychology, tangible explorations on real-world data remain scant. Concurrently, users on social media platforms are increasingly vocalizing personal sentiments; under specific thematic umbrellas, these sentiments often manifest as negative emotions, sometimes escalating to suicidal inclinations. Timely discernment of such cognitive distortions and suicidal risks is crucial to effectively intervene and potentially avert dire circumstances. Our study ventured into this realm by experimenting on two pivotal tasks: suicidal risk and cognitive distortion identification on Chinese social media platforms. Using supervised learning as a baseline, we examined and contrasted the efficacy of large language models via three distinct strategies: zero-shot, few-shot, and fine-tuning. Our findings revealed a discernible performance gap between the large language models and traditional supervised learning approaches, primarily attributed to the models' inability to fully grasp subtle categories. Notably, while GPT-4 outperforms its counterparts in multiple scenarios, GPT-3.5 shows significant enhancement in suicide risk classification after fine-tuning. To our knowledge, this investigation stands as the maiden attempt at gauging large language models on Chinese social media tasks. This study underscores the forward-looking and transformative implications of using large language models in the field of psychology. It lays the groundwork for future applications in psychological research and practice.

Accurate segmentation of punctate white matter lesions (PWMLs) are fundamental for the timely diagnosis and treatment of related developmental disorders. Automated PWMLs segmentation from infant brain MR images is challenging, considering that the lesions are typically small and low-contrast, and the number of lesions may dramatically change across subjects. Existing learning-based methods directly apply general network architectures to this challenging task, which may fail to capture detailed positional information of PWMLs, potentially leading to severe under-segmentations. In this paper, we propose to leverage the idea of counterfactual reasoning coupled with the auxiliary task of brain tissue segmentation to learn fine-grained positional and morphological representations of PWMLs for accurate localization and segmentation. A simple and easy-to-implement deep-learning framework (i.e., DeepPWML) is accordingly designed. It combines the lesion counterfactual map with the tissue probability map to train a lightweight PWML segmentation network, demonstrating state-of-the-art performance on a real-clinical dataset of infant T1w MR images. The code is available at \href{https://github.com/ladderlab-xjtu/DeepPWML}{https://github.com/ladderlab-xjtu/DeepPWML}.

Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases. An important metric of cardiac function is the left ventricular ejection fraction (EF), where lower EF is associated with cardiomyopathy. Echocardiography is a popular diagnostic tool in cardiology, with ultrasound being a low-cost, real-time, and non-ionizing technology. However, human assessment of echocardiograms for calculating EF is time-consuming and expertise-demanding, raising the need for an automated approach. In this work, we propose using the M(otion)-mode of echocardiograms for estimating the EF and classifying cardiomyopathy. We generate multiple artificial M-mode images from a single echocardiogram and combine them using off-the-shelf model architectures. Additionally, we extend contrastive learning (CL) to cardiac imaging to learn meaningful representations from exploiting structures in unlabeled data allowing the model to achieve high accuracy, even with limited annotations. Our experiments show that the supervised setting converges with only ten modes and is comparable to the baseline method while bypassing its cumbersome training process and being computationally much more efficient. Furthermore, CL using M-mode images is helpful for limited data scenarios, such as having labels for only 200 patients, which is common in medical applications.

We introduce two new extensions to the beam search algorithm based on conformal predictions (CP) to produce sets of sequences with theoretical coverage guarantees. The first method is very simple and proposes dynamically-sized subsets of beam search results but, unlike typical CP procedures, has an upper bound on the achievable guarantee depending on a post-hoc calibration measure. Our second algorithm introduces the conformal set prediction procedure as part of the decoding process, producing a variable beam width which adapts to the current uncertainty. While more complex, this procedure can achieve coverage guarantees selected a priori. We provide marginal coverage bounds for each method, and evaluate them empirically on a selection of tasks drawing from natural language processing and chemistry.

Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity arises from the need for multi-fingered robotic hands to dynamically establish and break contacts, balance non-prehensile forces, and control large degrees of freedom. Reinforcement learning (RL) offers a promising approach due to its general applicability and capacity to autonomously acquire optimal manipulation strategies. However, its real-world application is often hindered by the necessity to generate a large number of samples, reset the environment, and obtain reward signals. In this work, we introduce an efficient system for learning dexterous manipulation skills with RL to alleviate these challenges. The main idea of our approach is the integration of recent advances in sample-efficient RL and replay buffer bootstrapping. This combination allows us to utilize data from different tasks or objects as a starting point for training new tasks, significantly improving learning efficiency. Additionally, our system completes the real-world training cycle by incorporating learned resets via an imitation-based pickup policy as well as learned reward functions, eliminating the need for manual resets and reward engineering. We demonstrate the benefits of reusing past data as replay buffer initialization for new tasks, for instance, the fast acquisition of intricate manipulation skills in the real world on a four-fingered robotic hand. (Videos: https://sites.google.com/view/reboot-dexterous)

Explainable Artificial Intelligence (XAI) has become increasingly significant for improving the interpretability and trustworthiness of machine learning models. While saliency maps have stolen the show for the last few years in the XAI field, their ability to reflect models' internal processes has been questioned. Although less in the spotlight, example-based XAI methods have continued to improve. It encompasses methods that use examples as explanations for a machine learning model's predictions. This aligns with the psychological mechanisms of human reasoning and makes example-based explanations natural and intuitive for users to understand. Indeed, humans learn and reason by forming mental representations of concepts based on examples. This paper provides an overview of the state-of-the-art in natural example-based XAI, describing the pros and cons of each approach. A "natural" example simply means that it is directly drawn from the training data without involving any generative process. The exclusion of methods that require generating examples is justified by the need for plausibility which is in some regards required to gain a user's trust. Consequently, this paper will explore the following family of methods: similar examples, counterfactual and semi-factual, influential instances, prototypes, and concepts. In particular, it will compare their semantic definition, their cognitive impact, and added values. We hope it will encourage and facilitate future work on natural example-based XAI.

Adversarial examples, inputs designed to induce worst-case behavior in machine learning models, have been extensively studied over the past decade. Yet, our understanding of this phenomenon stems from a rather fragmented pool of knowledge; at present, there are a handful of attacks, each with disparate assumptions in threat models and incomparable definitions of optimality. In this paper, we propose a systematic approach to characterize worst-case (i.e., optimal) adversaries. We first introduce an extensible decomposition of attacks in adversarial machine learning by atomizing attack components into surfaces and travelers. With our decomposition, we enumerate over components to create 576 attacks (568 of which were previously unexplored). Next, we propose the Pareto Ensemble Attack (PEA): a theoretical attack that upper-bounds attack performance. With our new attacks, we measure performance relative to the PEA on: both robust and non-robust models, seven datasets, and three extended lp-based threat models incorporating compute costs, formalizing the Space of Adversarial Strategies. From our evaluation we find that attack performance to be highly contextual: the domain, model robustness, and threat model can have a profound influence on attack efficacy. Our investigation suggests that future studies measuring the security of machine learning should: (1) be contextualized to the domain & threat models, and (2) go beyond the handful of known attacks used today.

Accurate acquisition of crowd flow at Points of Interest (POIs) is pivotal for effective traffic management, public service, and urban planning. Despite this importance, due to the limitations of urban sensing techniques, the data quality from most sources is inadequate for monitoring crowd flow at each POI. This renders the inference of accurate crowd flow from low-quality data a critical and challenging task. The complexity is heightened by three key factors: 1) \emph{The scarcity and rarity of labeled data}, 2) \emph{The intricate spatio-temporal dependencies among POIs}, and 3) \emph{The myriad correlations between precise crowd flow and GPS reports}. To address these challenges, we recast the crowd flow inference problem as a self-supervised attributed graph representation learning task and introduce a novel \underline{C}ontrastive \underline{S}elf-learning framework for \underline{S}patio-\underline{T}emporal data (\model). Our approach initiates with the construction of a spatial adjacency graph founded on the POIs and their respective distances. We then employ a contrastive learning technique to exploit large volumes of unlabeled spatio-temporal data. We adopt a swapped prediction approach to anticipate the representation of the target subgraph from similar instances. Following the pre-training phase, the model is fine-tuned with accurate crowd flow data. Our experiments, conducted on two real-world datasets, demonstrate that the \model pre-trained on extensive noisy data consistently outperforms models trained from scratch.

Parameterized quantum circuits as machine learning models are typically well described by their representation as a partial Fourier series of the input features, with frequencies uniquely determined by the feature map's generator Hamiltonians. Ordinarily, these data-encoding generators are chosen in advance, fixing the space of functions that can be represented. In this work we consider a generalization of quantum models to include a set of trainable parameters in the generator, leading to a trainable frequency (TF) quantum model. We numerically demonstrate how TF models can learn generators with desirable properties for solving the task at hand, including non-regularly spaced frequencies in their spectra and flexible spectral richness. Finally, we showcase the real-world effectiveness of our approach, demonstrating an improved accuracy in solving the Navier-Stokes equations using a TF model with only a single parameter added to each encoding operation. Since TF models encompass conventional fixed frequency models, they may offer a sensible default choice for variational quantum machine learning.

Rational and neural network based approximations are efficient tools in modern approximation. These approaches are able to produce accurate approximations to nonsmooth and non-Lipschitz functions, including multivariate domain functions. In this paper we compare the efficiency of function approximation using rational approximation, neural network and their combinations. It was found that rational approximation is superior to neural network based approaches with the same number of decision variables. Our numerical experiments demonstrate the efficiency of rational approximation, even when the number of approximation parameters (that is, the dimension of the corresponding optimisation problems) is small. Another important contribution of this paper lies in the improvement of rational approximation algorithms. Namely, the optimisation based algorithms for rational approximation can be adjusted to in such a way that the conditioning number of the constraint matrices are controlled. This simple adjustment enables us to work with high dimension optimisation problems and improve the design of the neural network. The main strength of neural networks is in their ability to handle models with a large number of variables: complex models are decomposed in several simple optimisation problems. Therefore the the large number of decision variables is in the nature of neural networks.

We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, focusing on evolutionary agents in Gymnasium (game) simulators -- where fitness computation is costly. Maintaining a dataset of sampled individuals along with their actual fitness scores, we continually update throughout an evolutionary run a fitness-approximation ML model. We compare different methods for: 1) switching between actual and approximate fitness, 2) sampling the population, and 3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than that of the fully run GA -- depending on the ratio of approximate-to-actual-fitness computation. Our approach is generic and can be easily applied to many different domains.

Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by using neural networks to extract spatiotemporal correlations, their effectiveness depends on the quality of the graph structures used to represent the spatial topology of the traffic network. In this work, we propose a novel approach for traffic prediction that embeds time-varying dynamic Bayesian network to capture the fine spatiotemporal topology of traffic data. We then use graph convolutional networks to generate traffic forecasts. To enable our method to efficiently model nonlinear traffic propagation patterns, we develop a deep learning-based module as a hyper-network to generate stepwise dynamic causal graphs. Our experimental results on a real traffic dataset demonstrate the superior prediction performance of the proposed method. The code is available at https://github.com/MonBG/DCGCN.

$ $Acoustic-to-articulatory inversion (AAI) involves mapping from the acoustic space to the articulatory space. Signal-processing features like the MFCCs, have been widely used for the AAI task. For subjects with dysarthric speech, AAI is challenging because of an imprecise and indistinct pronunciation. In this work, we perform AAI for dysarthric speech using representations from pre-trained self-supervised learning (SSL) models. We demonstrate the impact of different pre-trained features on this challenging AAI task, at low-resource conditions. In addition, we also condition x-vectors to the extracted SSL features to train a BLSTM network. In the seen case, we experiment with three AAI training schemes (subject-specific, pooled, and fine-tuned). The results, consistent across training schemes, reveal that DeCoAR, in the fine-tuned scheme, achieves a relative improvement of the Pearson Correlation Coefficient (CC) by ${\sim}$1.81\% and ${\sim}$4.56\% for healthy controls and patients, respectively, over MFCCs. In the unseen case, we observe similar average trends for different SSL features. Overall, SSL networks like wav2vec, APC, and DeCoAR, which are trained with feature reconstruction or future timestep prediction tasks, perform well in predicting dysarthric articulatory trajectories.

Multivariate time series classification is an important computational task arising in applications where data is recorded over time and over multiple channels. For example, a smartwatch can record the acceleration and orientation of a person's motion, and these signals are recorded as multivariate time series. We can classify this data to understand and predict human movement and various properties such as fitness levels. In many applications classification alone is not enough, we often need to classify but also understand what the model learns (e.g., why was a prediction given, based on what information in the data). The main focus of this paper is on analysing and evaluating explanation methods tailored to Multivariate Time Series Classification (MTSC). We focus on saliency-based explanation methods that can point out the most relevant channels and time series points for the classification decision. We analyse two popular and accurate multivariate time series classifiers, ROCKET and dResNet, as well as two popular explanation methods, SHAP and dCAM. We study these methods on 3 synthetic datasets and 2 real-world datasets and provide a quantitative and qualitative analysis of the explanations provided. We find that flattening the multivariate datasets by concatenating the channels works as well as using multivariate classifiers directly and adaptations of SHAP for MTSC work quite well. Additionally, we also find that the popular synthetic datasets we used are not suitable for time series analysis.

Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps. We propose a faster and cheaper approach that adds noise not until the data become pure random noise, but until they reach a hidden noisy data distribution that we can confidently learn. Then, we use fewer reverse steps to generate data by starting from this hidden distribution that is made similar to the noisy data. We reveal that the proposed model can be cast as an adversarial auto-encoder empowered by both the diffusion process and a learnable implicit prior. Experimental results show even with a significantly smaller number of reverse diffusion steps, the proposed truncated diffusion probabilistic models can provide consistent improvements over the non-truncated ones in terms of performance in both unconditional and text-guided image generations.

The evolution of Generative AI and the capabilities of the newly released Large Language Models (LLMs) open new opportunities in software engineering. However, they also lead to new challenges in cybersecurity. Recently, researchers have shown the possibilities of using LLMs such as ChatGPT to generate malicious content that can directly be exploited or guide inexperienced hackers to weaponize tools and code. These studies covered scenarios that still require the attacker to be in the middle of the loop. In this study, we leverage openly available plugins and use an LLM as proxy between the attacker and the victim. We deliver a proof-of-concept where ChatGPT is used for the dissemination of malicious software while evading detection, alongside establishing the communication to a command and control (C2) server to receive commands to interact with a victim's system. Finally, we present the general approach as well as essential elements in order to stay undetected and make the attack a success. This proof-of-concept highlights significant cybersecurity issues with openly available plugins and LLMs, which require the development of security guidelines, controls, and mitigation strategies.

Short-term load forecasting is of paramount importance in the efficient operation and planning of power systems, given its inherent non-linear and dynamic nature. Recent strides in deep learning have shown promise in addressing this challenge. However, these methods often grapple with hyperparameter sensitivity, opaqueness in interpretability, and high computational overhead for real-time deployment. In this paper, I propose a novel solution that surmounts these obstacles. Our approach harnesses the power of the Particle-Swarm Optimization algorithm to autonomously explore and optimize hyperparameters, a Multi-Head Attention mechanism to discern the salient features crucial for accurate forecasting, and a streamlined framework for computational efficiency. Our method undergoes rigorous evaluation using a genuine electricity demand dataset. The results underscore its superiority in terms of accuracy, robustness, and computational efficiency. Notably, our Mean Absolute Percentage Error of 1.9376 marks a significant advancement over existing state-of-the-art approaches, heralding a new era in short-term load forecasting.

It is shown that a class of optical physical unclonable functions (PUFs) can be learned to arbitrary precision with arbitrarily high probability, even in the presence of noise, given access to polynomially many challenge-response pairs and polynomially bounded computational power, under mild assumptions about the distributions of the noise and challenge vectors. This extends the results of Rh\"uramir et al. (2013), who showed a subset of this class of PUFs to be learnable in polynomial time in the absence of noise, under the assumption that the optics of the PUF were either linear or had negligible nonlinear effects. We derive polynomial bounds for the required number of samples and the computational complexity of a linear regression algorithm, based on size parameters of the PUF, the distributions of the challenge and noise vectors, and the probability and accuracy of the regression algorithm, with a similar analysis to one done by Bootle et al. (2018), who demonstrated a learning attack on a poorly implemented version of the Learning With Errors problem.

While recommender systems have significantly benefited from implicit feedback, they have often missed the nuances of multi-behavior interactions between users and items. Historically, these systems either amalgamated all behaviors, such as \textit{impression} (formerly \textit{view}), \textit{add-to-cart}, and \textit{buy}, under a singular 'interaction' label, or prioritized only the target behavior, often the \textit{buy} action, discarding valuable auxiliary signals. Although recent advancements tried addressing this simplification, they primarily gravitated towards optimizing the target behavior alone, battling with data scarcity. Additionally, they tended to bypass the nuanced hierarchy intrinsic to behaviors. To bridge these gaps, we introduce the \textbf{H}ierarchical \textbf{M}ulti-behavior \textbf{G}raph Attention \textbf{N}etwork (HMGN). This pioneering framework leverages attention mechanisms to discern information from both inter and intra-behaviors while employing a multi-task Hierarchical Bayesian Personalized Ranking (HBPR) for optimization. Recognizing the need for scalability, our approach integrates a specialized multi-behavior sub-graph sampling technique. Moreover, the adaptability of HMGN allows for the seamless inclusion of knowledge metadata and time-series data. Empirical results attest to our model's prowess, registering a notable performance boost of up to 64\% in NDCG@100 metrics over conventional graph neural network methods.

Bootstrapping is behind much of the successes of deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values. Target Networks are employed to stabilize training by using an additional set of lagging parameters to estimate the target values. Despite the popularity of Target Networks, their effect on the optimization is still misunderstood. In this work, we show that they act as an implicit regularizer which can be beneficial in some cases, but also have disadvantages such as being inflexible and can result in instabilities, even when vanilla TD(0) converges. To overcome these issues, we propose an explicit Functional Regularization alternative that is flexible and a convex regularizer in function space and we theoretically study its convergence. We conduct an experimental study across a range of environments, discount factors, and off-policiness data collections to investigate the effectiveness of the regularization induced by Target Networks and Functional Regularization in terms of performance, accuracy, and stability. Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement. Furthermore, adjusting both the regularization weight and the network update period in Functional Regularization can result in further performance improvements compared to solely adjusting the network update period as typically done with Target Networks. Our approach also enhances the ability to networks to recover accurate $Q$-values.

This paper considers an empirical risk minimization problem under heavy-tailed settings, where data does not have finite variance, but only has $p$-th moment with $p \in (1,2)$. Instead of using estimation procedure based on truncated observed data, we choose the optimizer by minimizing the risk value. Those risk values can be robustly estimated via using the remarkable Catoni's method (Catoni, 2012). Thanks to the structure of Catoni-type influence functions, we are able to establish excess risk upper bounds via using generalized generic chaining methods. Moreover, we take computational issues into consideration. We especially theoretically investigate two types of optimization methods, robust gradient descent algorithm and empirical risk-based methods. With an extensive numerical study, we find that the optimizer based on empirical risks via Catoni-style estimation indeed shows better performance than other baselines. It indicates that estimation directly based on truncated data may lead to unsatisfactory results.

Consider two forecasters, each making a single prediction for a sequence of events over time. We ask a relatively basic question: how might we compare these forecasters, either online or post-hoc, while avoiding unverifiable assumptions on how the forecasts and outcomes were generated? In this paper, we present a rigorous answer to this question by designing novel sequential inference procedures for estimating the time-varying difference in forecast scores. To do this, we employ confidence sequences (CS), which are sequences of confidence intervals that can be continuously monitored and are valid at arbitrary data-dependent stopping times ("anytime-valid"). The widths of our CSs are adaptive to the underlying variance of the score differences. Underlying their construction is a game-theoretic statistical framework, in which we further identify e-processes and p-processes for sequentially testing a weak null hypothesis -- whether one forecaster outperforms another on average (rather than always). Our methods do not make distributional assumptions on the forecasts or outcomes; our main theorems apply to any bounded scores, and we later provide alternative methods for unbounded scores. We empirically validate our approaches by comparing real-world baseball and weather forecasters.

In recent years, deep neural networks (DNNs) trained with transformed data have been applied to various applications such as privacy-preserving learning, access control, and adversarial defenses. However, the use of transformed data decreases the performance of models. Accordingly, in this paper, we propose a novel method for fine-tuning models with transformed images under the use of the vision transformer (ViT). The proposed domain adaptation method does not cause the accuracy degradation of models, and it is carried out on the basis of the embedding structure of ViT. In experiments, we confirmed that the proposed method prevents accuracy degradation even when using encrypted images with the CIFAR-10 and CIFAR-100 datasets.

Explicit antisymmetrization of a neural network is a potential candidate for a universal function approximator for generic antisymmetric functions, which are ubiquitous in quantum physics. However, this procedure is a priori factorially costly to implement, making it impractical for large numbers of particles. The strategy also suffers from a sign problem. Namely, due to near-exact cancellation of positive and negative contributions, the magnitude of the antisymmetrized function may be significantly smaller than before anti-symmetrization. We show that the anti-symmetric projection of a two-layer neural network can be evaluated efficiently, opening the door to using a generic antisymmetric layer as a building block in anti-symmetric neural network Ansatzes. This approximation is effective when the sign problem is controlled, and we show that this property depends crucially the choice of activation function under standard Xavier/He initialization methods. As a consequence, using a smooth activation function requires re-scaling of the neural network weights compared to standard initializations.

Safeguarding data from unauthorized exploitation is vital for privacy and security, especially in recent rampant research in security breach such as adversarial/membership attacks. To this end, \textit{unlearnable examples} (UEs) have been recently proposed as a compelling protection, by adding imperceptible perturbation to data so that models trained on them cannot classify them accurately on original clean distribution. Unfortunately, we find UEs provide a false sense of security, because they cannot stop unauthorized users from utilizing other unprotected data to remove the protection, by turning unlearnable data into learnable again. Motivated by this observation, we formally define a new threat by introducing \textit{learnable unauthorized examples} (LEs) which are UEs with their protection removed. The core of this approach is a novel purification process that projects UEs onto the manifold of LEs. This is realized by a new joint-conditional diffusion model which denoises UEs conditioned on the pixel and perceptual similarity between UEs and LEs. Extensive experiments demonstrate that LE delivers state-of-the-art countering performance against both supervised UEs and unsupervised UEs in various scenarios, which is the first generalizable countermeasure to UEs across supervised learning and unsupervised learning. Our code is available at \url{https://github.com/jiangw-0/LE_JCDP}.

The burgeoning growth of public domain data and the increasing complexity of deep learning model architectures have underscored the need for more efficient data representation and analysis techniques. This paper is motivated by the work of Helal (2023) and aims to present a comprehensive overview of tensorization. This transformative approach bridges the gap between the inherently multidimensional nature of data and the simplified 2-dimensional matrices commonly used in linear algebra-based machine learning algorithms. This paper explores the steps involved in tensorization, multidimensional data sources, various multiway analysis methods employed, and the benefits of these approaches. A small example of Blind Source Separation (BSS) is presented comparing 2-dimensional algorithms and a multiway algorithm in Python. Results indicate that multiway analysis is more expressive. Contrary to the intuition of the dimensionality curse, utilising multidimensional datasets in their native form and applying multiway analysis methods grounded in multilinear algebra reveal a profound capacity to capture intricate interrelationships among various dimensions while, surprisingly, reducing the number of model parameters and accelerating processing. A survey of the multi-away analysis methods and integration with various Deep Neural Networks models is presented using case studies in different domains.

We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that are necessary to articulate objects. ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose. Our framework unifies grasping and articulation within a single policy guided by a single hand pose reference. Moreover, to facilitate the training of the precise finger control required for articulation, we present a learning curriculum with increasing difficulty. It starts with single-hand manipulation of stationary objects and continues with multi-agent training including both hands and non-stationary objects. To evaluate our method, we introduce Dynamic Object Grasping and Articulation, a task that involves bringing an object into a target articulated pose. This task requires grasping, relocation, and articulation. We show our method's efficacy towards this task. We further demonstrate that our method can generate motions with noisy hand-object pose estimates from an off-the-shelf image-based regressor.

Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of these models in order for results to be comparable. However, there is a mismatch between the distributions of cases and difficulty level of segmentation tasks in public data sets compared to clinical practice. Common metrics fail to measure the impact of this mismatch, especially for clinical data sets that include low signal pathologies, a difficult segmentation task, and uncertain, small, or empty reference annotations. This limitation may result in ineffective research of machine learning practitioners in designing and optimizing models. Dimensions of evaluating clinical value include consideration of the uncertainty of reference annotations, independence from reference annotation volume size, and evaluation of classification of empty reference annotations. We study how uncertain, small, and empty reference annotations influence the value of metrics for medical image segmentation on an in-house data set regardless of the model. We examine metrics behavior on the predictions of a standard deep learning framework in order to identify metrics with clinical value. We compare to a public benchmark data set (BraTS 2019) with a high-signal pathology and certain, larger, and no empty reference annotations. We may show machine learning practitioners, how uncertain, small, or empty reference annotations require a rethinking of the evaluation and optimizing procedures. The evaluation code was released to encourage further analysis of this topic. https://github.com/SophieOstmeier/UncertainSmallEmpty.git

Learning for Demonstration (LfD) enables robots to acquire new skills by imitating expert demonstrations, allowing users to communicate their instructions in an intuitive manner. Recent progress in LfD often relies on kinesthetic teaching or teleoperation as the medium for users to specify the demonstrations. Kinesthetic teaching requires physical handling of the robot, while teleoperation demands proficiency with additional hardware. This paper introduces an alternative paradigm for LfD called Diagrammatic Teaching. Diagrammatic Teaching aims to teach robots novel skills by prompting the user to sketch out demonstration trajectories on 2D images of the scene, these are then synthesised as a generative model of motion trajectories in 3D task space. Additionally, we present the Ray-tracing Probabilistic Trajectory Learning (RPTL) framework for Diagrammatic Teaching. RPTL extracts time-varying probability densities from the 2D sketches, applies ray-tracing to find corresponding regions in 3D Cartesian space, and fits a probabilistic model of motion trajectories to these regions. New motion trajectories, which mimic those sketched by the user, can then be generated from the probabilistic model. We empirically validate our framework both in simulation and on real robots, which include a fixed-base manipulator and a quadruped-mounted manipulator.

Recent studies have verified that equivariant methods can significantly improve the data efficiency, generalizability, and robustness in robot learning. Meanwhile, denoising diffusion-based generative modeling has recently gained significant attention as a promising approach for robotic manipulation learning from demonstrations with stochastic behaviors. In this paper, we present Diffusion-EDFs, a novel approach that incorporates spatial roto-translation equivariance, i.e., SE(3)-equivariance to diffusion generative modeling. By integrating SE(3)-equivariance into our model architectures, we demonstrate that our proposed method exhibits remarkable data efficiency, requiring only 5 to 10 task demonstrations for effective end-to-end training. Furthermore, our approach showcases superior generalizability compared to previous diffusion-based manipulation methods.

As an essential component part of the Intelligent Transportation System (ITS), the Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one of the key technologies in the IoV, which has been widely used to provide traffic management services by analyzing timely and sensitive vehicle-related information. However, the current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server, which raises privacy concerns. To mitigate such privacy leakage, we first propose a federated learning-based framework, where well-trained local models are shared in the central server. However, since edge devices usually have limited computing power, plus a strict requirement of low latency in IoVs, we further propose a sparse training process on edge devices, which can effectively lighten the model, and ensure its training efficiency on edge devices, thereby reducing communication overheads. In addition, due to the diverse computing capabilities and dynamic environment, different sparsity rates are applied to edge devices. To further guarantee the performance, we propose, FedWeg, an improved aggregation scheme based on FedAvg, which is designed by the inverse ratio of sparsity rates. Experiments on the real-life dataset using YOLO show that the proposed scheme can achieve the required object detection rate while saving considerable communication costs.

Instruction-tuned Large Language Models (LLMs) have recently showcased remarkable ability to generate fitting responses to natural language instructions. However, an open research question concerns the inherent biases of trained models and their responses. For instance, if the data used to tune an LLM is dominantly written by persons with a specific political bias, we might expect generated answers to share this bias. Current research work seeks to de-bias such models, or suppress potentially biased answers. With this demonstration, we take a different view on biases in instruction-tuning: Rather than aiming to suppress them, we aim to make them explicit and transparent. To this end, we present OpinionGPT, a web demo in which users can ask questions and select all biases they wish to investigate. The demo will answer this question using a model fine-tuned on text representing each of the selected biases, allowing side-by-side comparison. To train the underlying model, we identified 11 different biases (political, geographic, gender, age) and derived an instruction-tuning corpus in which each answer was written by members of one of these demographics. This paper presents OpinionGPT, illustrates how we trained the bias-aware model and showcases the web application (available at https://opiniongpt.informatik.hu-berlin.de).

We present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tuning, different from which, our ImageBind-LLM can respond to multi-modality conditions, including audio, 3D point clouds, video, and their embedding-space arithmetic by only image-text alignment training. During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder. Then, the image features transformed by the bind network are added to word tokens of all layers in LLaMA, which progressively injects visual instructions via an attention-free and zero-initialized gating mechanism. Aided by the joint embedding of ImageBind, the simple image-text training enables our model to exhibit superior multi-modality instruction-following capabilities. During inference, the multi-modality inputs are fed into the corresponding ImageBind encoders, and processed by a proposed visual cache model for further cross-modal embedding enhancement. The training-free cache model retrieves from three million image features extracted by ImageBind, which effectively mitigates the training-inference modality discrepancy. Notably, with our approach, ImageBind-LLM can respond to instructions of diverse modalities and demonstrate significant language generation quality. Code is released at https://github.com/OpenGVLab/LLaMA-Adapter.

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal-dual contextual Bayesian optimization algorithm is proposed that achieves sublinear cumulative regret with respect to the dynamic optimal solution under certain regularity conditions. Furthermore, the algorithm achieves zero time-average constraint violation, ensuring that the average value of the constraint function satisfies the desired constraint. The method is applied to both sampled instances from Gaussian processes and a continuous stirred tank reactor parameter tuning problem; simulation results show that the method simultaneously provides close-to-optimal performance and maintains constraint feasibility on average. This contrasts current state-of-the-art methods, which either suffer from large cumulative regret or severe constraint violations for the case studies presented.

Reinforcement learning algorithms often require finiteness of state and action spaces in Markov decision processes (MDPs) (also called controlled Markov chains) and various efforts have been made in the literature towards the applicability of such algorithms for continuous state and action spaces. In this paper, we show that under very mild regularity conditions (in particular, involving only weak continuity of the transition kernel of an MDP), Q-learning for standard Borel MDPs via quantization of states and actions (called Quantized Q-Learning) converges to a limit, and furthermore this limit satisfies an optimality equation which leads to near optimality with either explicit performance bounds or which are guaranteed to be asymptotically optimal. Our approach builds on (i) viewing quantization as a measurement kernel and thus a quantized MDP as a partially observed Markov decision process (POMDP), (ii) utilizing near optimality and convergence results of Q-learning for POMDPs, and (iii) finally, near-optimality of finite state model approximations for MDPs with weakly continuous kernels which we show to correspond to the fixed point of the constructed POMDP. Thus, our paper presents a very general convergence and approximation result for the applicability of Q-learning for continuous MDPs.

Previous studies have typically assumed that large language models are unable to accurately perform arithmetic operations, particularly multiplication of >8 digits, and operations involving decimals and fractions, without the use of calculator tools. This paper aims to challenge this misconception. With sufficient training data, a 2 billion-parameter language model can accurately perform multi-digit arithmetic operations with almost 100% accuracy without data leakage, significantly surpassing GPT-4 (whose multi-digit multiplication accuracy is only 4.3%). We also demonstrate that our MathGLM, fine-tuned from GLM-10B on a dataset with additional multi-step arithmetic operations and math problems described in text, achieves similar performance to GPT-4 on a 5,000-samples Chinese math problem test set.

Hyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML). In practice, users are often interested in multi-objective (MO) problems, i.e., optimizing potentially conflicting objectives, like accuracy and energy consumption. To tackle this, the vast majority of MO-ML algorithms return a Pareto front of non-dominated machine learning models to the user. Optimizing the hyperparameters of such algorithms is non-trivial as evaluating a hyperparameter configuration entails evaluating the quality of the resulting Pareto front. In literature, there are known indicators that assess the quality of a Pareto front (e.g., hypervolume, R2) by quantifying different properties (e.g., volume, proximity to a reference point). However, choosing the indicator that leads to the desired Pareto front might be a hard task for a user. In this paper, we propose a human-centered interactive HPO approach tailored towards multi-objective ML leveraging preference learning to extract desiderata from users that guide the optimization. Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator. Concretely, we leverage pairwise comparisons of distinct Pareto fronts to learn such an appropriate quality indicator. Then, we optimize the hyperparameters of the underlying MO-ML algorithm towards this learned indicator using a state-of-the-art HPO approach. In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.

The Lipschitz bound, a technique from robust statistics, can limit the maximum changes in the output concerning the input, taking into account associated irrelevant biased factors. It is an efficient and provable method for examining the output stability of machine learning models without incurring additional computation costs. Recently, Graph Neural Networks (GNNs), which operate on non-Euclidean data, have gained significant attention. However, no previous research has investigated the GNN Lipschitz bounds to shed light on stabilizing model outputs, especially when working on non-Euclidean data with inherent biases. Given the inherent biases in common graph data used for GNN training, it poses a serious challenge to constraining the GNN output perturbations induced by input biases, thereby safeguarding fairness during training. Recently, despite the Lipschitz constant's use in controlling the stability of Euclideanneural networks, the calculation of the precise Lipschitz constant remains elusive for non-Euclidean neural networks like GNNs, especially within fairness contexts. To narrow this gap, we begin with the general GNNs operating on an attributed graph, and formulate a Lipschitz bound to limit the changes in the output regarding biases associated with the input. Additionally, we theoretically analyze how the Lipschitz constant of a GNN model could constrain the output perturbations induced by biases learned from data for fairness training. We experimentally validate the Lipschitz bound's effectiveness in limiting biases of the model output. Finally, from a training dynamics perspective, we demonstrate why the theoretical Lipschitz bound can effectively guide the GNN training to better trade-off between accuracy and fairness.

Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data instances, which can be used to address chronic disease screening problem. State-of-the-art PU learning methods have resulted in the development of various risk estimators, yet they neglect the differences among distinct populations. To address this issue, we present a novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed to take into account communities such as different age or income brackets, in tasks of chronic disease prediction. We propose a novel approach for binary decision-making, which hierarchically builds community-based PU models and then aggregates their deliverables. Our method can explicate each PU model on the tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery data augmentation strategy enables sufficient training of the model in individual communities. Additionally, the proposed approach includes an adversarial PU risk estimator to capture hierarchical PU-relationships, and a model fusion network that integrates data from each tree path, resulting in robust binary classification results. We demonstrate the superior performance of PUtree as well as its variants on two benchmarks and a new diabetes-prediction dataset.

The choice of the objective function is crucial in emerging high-quality representations from self-supervised learning. This paper investigates how different formulations of the Barlow Twins (BT) objective impact downstream task performance for speech data. We propose Modified Barlow Twins (MBT) with normalized latents to enforce scale-invariance and evaluate on speaker identification, gender recognition and keyword spotting tasks. Our results show MBT improves representation generalization over original BT, especially when fine-tuning with limited target data. This highlights the importance of designing objectives that encourage invariant and transferable representations. Our analysis provides insights into how the BT learning objective can be tailored to produce speech representations that excel when adapted to new downstream tasks. This study is an important step towards developing reusable self-supervised speech representations.

Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by industrial entities. However, in times of threats to its supply, it is also a critical element that guarantees the supply of this raw material to meet individual consumers' needs, ensuring society's energy security. This article introduces a novel multistep ahead forecasting of natural gas consumption with change point detection integration for model collection selection with continual learning capabilities using data stream processing. The performance of the forecasting models based on the proposed approach is evaluated in a complex real-world use case of natural gas consumption forecasting. We employed Hoeffding tree predictors as forecasting models and the Pruned Exact Linear Time (PELT) algorithm for the change point detection procedure. The change point detection integration enables selecting a different model collection for successive time frames. Thus, three model collection selection procedures (with and without an error feedback loop) are defined and evaluated for forecasting scenarios with various densities of detected change points. These models were compared with change point agnostic baseline approaches. Our experiments show that fewer change points result in a lower forecasting error regardless of the model collection selection procedure employed. Also, simpler model collection selection procedures omitting forecasting error feedback leads to more robust forecasting models suitable for continual learning tasks.

Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.

To determine if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independently segmented hypodensity that reflected the ischemic core on each scan. The neuroradiologist with the most experience (expert A) served as the ground truth for deep learning model training. Two additional neuroradiologists (experts B and C) segmentations were used for data testing. The 232 studies were randomly split into training and test sets. The training set was further randomly divided into 5 folds with training and validation sets. A 3-dimensional CNN architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics using non-inferiority thresholds of 20%, 3ml, and 3mm. The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. The final model performance for the ischemic core segmentation task reached a performance of 0.46+-0.09 Surface Dice at Tolerance 5mm and 0.47+-0.13 Dice when trained on expert A. Compared to the two test neuroradiologists the model-expert agreement was non-inferior to the inter-expert agreement, p < 0.05. The CNN accurately delineates the hypodense ischemic core on NCCT in acute ischemic stroke patients with an accuracy comparable to neuroradiologists.

This paper presents a novel deep learning approach for analyzing massive underwater acoustic data by leveraging a model trained on a broad spectrum of non-underwater (aerial) sounds. Recognizing the challenge in labeling vast amounts of underwater data, we propose a two-fold methodology to accelerate this labor-intensive procedure. The first part of our approach involves PCA and UMAP visualization of the underwater data using the feature vectors of an aerial sound recognition model. This enables us to cluster the data in a two dimensional space and listen to points within these clusters to understand their defining characteristics. This innovative method simplifies the process of selecting candidate labels for further training. In the second part, we train a neural network model using both the selected underwater data and the non-underwater dataset. We conducted a quantitative analysis to measure the precision, recall, and F1 score of our model for recognizing airgun sounds, a common type of underwater sound. The F1 score achieved by our model exceeded 84.3%, demonstrating the effectiveness of our approach in analyzing underwater acoustic data. The methodology presented in this paper holds significant potential to reduce the amount of labor required in underwater data analysis and opens up new possibilities for further research in the field of cross-domain data analysis.

This paper examines some common problems in Human-Robot Interaction (HRI) causing failures and troubles in Chat. A given use case's design decisions start with the suitable robot, the suitable chatting model, identifying common problems that cause failures, identifying potential solutions, and planning continuous improvement. In conclusion, it is recommended to use a closed-loop control algorithm that guides the use of trained Artificial Intelligence (AI) pre-trained models and provides vocabulary filtering, re-train batched models on new datasets, learn online from data streams, and/or use reinforcement learning models to self-update the trained models and reduce errors.

Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking is exceptionally computationally challenging and fielded solutions, relying on traditional algorithms, do not scale linearly. Machine Learning (ML) assisted solutions are a promising answer. We argue that a complexity-reduced problem description and the data representing it, will facilitate the solution exploration workflow. We provide the REDuced VIrtual Detector (REDVID) as a complexity-reduced detector model and particle collision event simulator combo. REDVID is intended as a simulation-in-the-loop, to both generate synthetic data efficiently and to simplify the challenge of ML model design. The fully parametric nature of our tool, with regards to system-level configuration, while in contrast to physics-accurate simulations, allows for the generation of simplified data for research and education, at different levels. Resulting from the reduced complexity, we showcase the computational efficiency of REDVID by providing the computational cost figures for a multitude of simulation benchmarks. As a simulation and a generative tool for ML-assisted solution design, REDVID is highly flexible, reusable and open-source. Reference data sets generated with REDVID are publicly available.

Given pairwise comparisons between multiple items, how to rank them so that the ranking matches the observations? This problem, known as rank aggregation, has found many applications in sports, recommendation systems, and other web applications. As it is generally NP-hard to find a global ranking that minimizes the mismatch (known as the Kemeny optimization), we focus on the Erd\"os-R\'enyi outliers (ERO) model for this ranking problem. Here, each pairwise comparison is a corrupted copy of the true score difference. We investigate spectral ranking algorithms that are based on unnormalized and normalized data matrices. The key is to understand their performance in recovering the underlying scores of each item from the observed data. This reduces to deriving an entry-wise perturbation error bound between the top eigenvectors of the unnormalized/normalized data matrix and its population counterpart. By using the leave-one-out technique, we provide a sharper $\ell_{\infty}$-norm perturbation bound of the eigenvectors and also derive an error bound on the maximum displacement for each item, with only $\Omega(n\log n)$ samples. Our theoretical analysis improves upon the state-of-the-art results in terms of sample complexity, and our numerical experiments confirm these theoretical findings.

Drift in machine learning refers to the phenomenon where the statistical properties of data or context, in which the model operates, change over time leading to a decrease in its performance. Therefore, maintaining a constant monitoring process for machine learning model performance is crucial in order to proactively prevent any potential performance regression. However, supervised drift detection methods require human annotation and consequently lead to a longer time to detect and mitigate the drift. In our proposed unsupervised drift detection method, we follow a two step process. Our first step involves encoding a sample of production data as the target distribution, and the model training data as the reference distribution. In the second step, we employ a kernel-based statistical test that utilizes the maximum mean discrepancy (MMD) distance metric to compare the reference and target distributions and estimate any potential drift. Our method also identifies the subset of production data that is the root cause of the drift. The models retrained using these identified high drift samples show improved performance on online customer experience quality metrics.

Current machine learning methods struggle to solve Bongard problems, which are a type of IQ test that requires deriving an abstract "concept" from a set of positive and negative "support" images, and then classifying whether or not a new query image depicts the key concept. On Bongard-HOI, a benchmark for natural-image Bongard problems, existing methods have only reached 66% accuracy (where chance is 50%). Low accuracy is often attributed to neural nets' lack of ability to find human-like symbolic rules. In this work, we point out that many existing methods are forfeiting accuracy due to a much simpler problem: they do not incorporate information contained in the support set as a whole, and rely instead on information extracted from individual supports. This is a critical issue, because unlike in few-shot learning tasks concerning object classification, the "key concept" in a typical Bongard problem can only be distinguished using multiple positives and multiple negatives. We explore a variety of simple methods to take this cross-image context into account, and demonstrate substantial gains over prior methods, leading to new state-of-the-art performance on Bongard-LOGO (75.3%) and Bongard-HOI (72.45%) and strong performance on the original Bongard problem set (60.84%).

In lending, where prices are specific to both customers and products, having a well-functioning personalized pricing policy in place is essential to effective business making. Typically, such a policy must be derived from observational data, which introduces several challenges. While the problem of ``endogeneity'' is prominently studied in the established pricing literature, the problem of selection bias (or, more precisely, bid selection bias) is not. We take a step towards understanding the effects of selection bias by posing pricing as a problem of causal inference. Specifically, we consider the reaction of a customer to price a treatment effect. In our experiments, we simulate varying levels of selection bias on a semi-synthetic dataset on mortgage loan applications in Belgium. We investigate the potential of parametric and nonparametric methods for the identification of individual bid-response functions. Our results illustrate how conventional methods such as logistic regression and neural networks suffer adversely from selection bias. In contrast, we implement state-of-the-art methods from causal machine learning and show their capability to overcome selection bias in pricing data.

We present a data-driven model for ground-motion synthesis using a Generative Adversarial Neural Operator (GANO) that combines recent advancements in machine learning and open access strong motion data sets to generate three-component acceleration time histories conditioned on moment magnitude ($M$), rupture distance ($R_{rup}$), time-average shear-wave velocity at the top $30m$ ($V_{S30}$), and tectonic environment or style of faulting. We use Neural Operators, a resolution invariant architecture that guarantees that the model training is independent of the data sampling frequency. We first present the conditional ground-motion synthesis algorithm (referred to heretofore as cGM-GANO) and discuss its advantages compared to previous work. Next, we verify the cGM-GANO framework using simulated ground motions generated with the Southern California Earthquake Center (SCEC) Broadband Platform (BBP). We lastly train cGM-GANO on a KiK-net dataset from Japan, showing that the framework can recover the magnitude, distance, and $V_{S30}$ scaling of Fourier amplitude and pseudo-spectral accelerations. We evaluate cGM-GANO through residual analysis with the empirical dataset as well as by comparison with conventional Ground Motion Models (GMMs) for selected ground motion scenarios. Results show that cGM-GANO produces consistent median scaling with the GMMs for the corresponding tectonic environments. The largest misfit is observed at short distances due to the scarcity of training data. With the exception of short distances, the aleatory variability of the response spectral ordinates is also well captured, especially for subduction events due to the adequacy of training data. Applications of the presented framework include generation of risk-targeted ground motions for site-specific engineering applications.

Existing approaches for classifying dynamic graphs either lift graph kernels to the temporal domain, or use graph neural networks (GNNs). However, current baselines have scalability issues, cannot handle a changing node set, or do not take edge weight information into account. We propose filtration surfaces, a novel method that is scalable and flexible, to alleviate said restrictions. We experimentally validate the efficacy of our model and show that filtration surfaces outperform previous state-of-the-art baselines on datasets that rely on edge weight information. Our method does so while being either completely parameter-free or having at most one parameter, and yielding the lowest overall standard deviation.

We study distribution testing in the standard access model and the conditional access model when the memory available to the testing algorithm is bounded. In both scenarios, the samples appear in an online fashion and the goal is to test the properties of distribution using an optimal number of samples subject to a memory constraint on how many samples can be stored at a given time. First, we provide a trade-off between the sample complexity and the space complexity for testing identity when the samples are drawn according to the conditional access oracle. We then show that we can learn a succinct representation of a monotone distribution efficiently with a memory constraint on the number of samples that are stored that is almost optimal. We also show that the algorithm for monotone distributions can be extended to a larger class of decomposable distributions.

This work proposes a robust Partial Domain Adaptation (PDA) framework that mitigates the negative transfer problem by incorporating a robust target-supervision strategy. It leverages ensemble learning and includes diverse, complementary label feedback, alleviating the effect of incorrect feedback and promoting pseudo-label refinement. Rather than relying exclusively on first-order moments for distribution alignment, our approach offers explicit objectives to optimize intra-class compactness and inter-class separation with the inferred source prototypes and highly-confident target samples in a domain-invariant fashion. Notably, we ensure source data privacy by eliminating the need to access the source data during the adaptation phase through a priori inference of source prototypes. We conducted a series of comprehensive experiments, including an ablation analysis, covering a range of partial domain adaptation tasks. Comprehensive evaluations on benchmark datasets corroborate our framework's enhanced robustness and generalization, demonstrating its superiority over existing state-of-the-art PDA approaches.

This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners - KG Builders, Analysts, and Consumers - each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.

In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based computational neural network tailored for medical image segmentation on IoT and edge platforms. Conventional U-Net-based models face challenges in meeting the speed and efficiency demands of real-time clinical applications, such as disease monitoring, radiation therapy, and image-guided surgery. LDMRes-Net overcomes these limitations with its remarkably low number of learnable parameters (0.072M), making it highly suitable for resource-constrained devices. The model's key innovation lies in its dual multi-residual block architecture, which enables the extraction of refined features on multiple scales, enhancing overall segmentation performance. To further optimize efficiency, the number of filters is carefully selected to prevent overlap, reduce training time, and improve computational efficiency. The study includes comprehensive evaluations, focusing on segmentation of the retinal image of vessels and hard exudates crucial for the diagnosis and treatment of ophthalmology. The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms. Such advances hold significant promise for improving healthcare outcomes and enabling real-time medical image analysis in resource-limited settings.

This paper proposes a novel approach for detecting objects using mobile robots in the context of the RoboCup Standard Platform League, with a primary focus on detecting the ball. The challenge lies in detecting a dynamic object in varying lighting conditions and blurred images caused by fast movements. To address this challenge, the paper presents a convolutional neural network architecture designed specifically for computationally constrained robotic platforms. The proposed CNN is trained to achieve high precision classification of single objects in image patches and to determine their precise spatial positions. The paper further integrates Early Exits into the existing high-precision CNN architecture to reduce the computational cost of easily rejectable cases in the background class. The training process involves a composite loss function based on confidence and positional losses with dynamic weighting and data augmentation. The proposed approach achieves a precision of 100% on the validation dataset and a recall of almost 87%, while maintaining an execution time of around 170 $\mu$s per hypotheses. By combining the proposed approach with an Early Exit, a runtime optimization of more than 28%, on average, can be achieved compared to the original CNN. Overall, this paper provides an efficient solution for an enhanced detection of objects, especially the ball, in computationally constrained robotic platforms.

Source camera identification in digital videos is the problem of associating an unknown digital video with its source device, within a closed set of possible devices. The existing techniques in source detection of digital videos try to find a fingerprint of the actual source in the video in form of PRNU (Photo Response Non--Uniformity), and match it against the SPN (Sensor Pattern Noise) of each possible device. The highest correlation indicates the correct source. We investigate the problem of identifying a video source through a feature based approach using machine learning. In this paper, we present a blind forensic technique of video source authentication and identification, based on feature extraction, feature selection and subsequent source classification. The main aim is to determine whether a claimed source for a video is actually its original source. If not, we identify its original source. Our experimental results prove the efficiency of the proposed method compared to traditional fingerprint based technique.

Federated learning (FL) is designed to preserve data privacy during model training, where the data remains on the client side (i.e., IoT devices), and only model updates of clients are shared iteratively for collaborative learning. However, this process is vulnerable to privacy attacks and Byzantine attacks: the local model updates shared throughout the FL network will leak private information about the local training data, and they can also be maliciously crafted by Byzantine attackers to disturb the learning. In this paper, we propose a new FL scheme that guarantees rigorous privacy and simultaneously enhances system robustness against Byzantine attacks. Our approach introduces sparsification- and momentum-driven variance reduction into the client-level differential privacy (DP) mechanism, to defend against Byzantine attackers. The security design does not violate the privacy guarantee of the client-level DP mechanism; hence, our approach achieves the same client-level DP guarantee as the state-of-the-art. We conduct extensive experiments on both IID and non-IID datasets and different tasks and evaluate the performance of our approach against different Byzantine attacks by comparing it with state-of-the-art defense methods. The results of our experiments show the efficacy of our framework and demonstrate its ability to improve system robustness against Byzantine attacks while achieving a strong privacy guarantee.

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the analysis continues to grow. In response to this need, we introduce Auto Bioinformatics Analysis (AutoBA), an autonomous AI agent based on a large language model designed explicitly for conventional omics data analysis. AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. Through rigorous validation by expert bioinformaticians, AutoBA's robustness and adaptability are affirmed across a diverse range of omics analysis cases, including whole genome sequencing (WGS), RNA sequencing (RNA-seq), single-cell RNA-seq, ChIP-seq, and spatial transcriptomics. AutoBA's unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA deploys the analysis locally, preserving data privacy. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents a convenient tool, offering robustness and adaptability for complex omics data analysis.

Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to develop general algorithms, the performance of each algorithm varies considerably over the problem instances. This paper provides an instance space analysis for sports timetabling, resulting in powerful insights into the strengths and weaknesses of eight state-of-the-art algorithms. Based on machine learning techniques, we propose an algorithm selection system that predicts which algorithm is likely to perform best when given the characteristics of a sports timetabling problem instance. Furthermore, we identify which characteristics are important in making that prediction, providing insights in the performance of the algorithms, and suggestions to further improve them. Finally, we assess the empirical hardness of the instances. Our results are based on large computational experiments involving about 50 years of CPU time on more than 500 newly generated problem instances.

Surveillance systems have emerged as crucial elements in upholding peace and security in the modern world. Their ubiquity aids in monitoring suspicious activities effectively. However, in densely populated environments, continuous active monitoring becomes impractical, necessitating the development of intelligent surveillance systems. AI integration in the surveillance domain was a big revolution, however, speed issues have prevented its widespread implementation in the field. It has been observed that quantum artificial intelligence has led to a great breakthrough. Quantum artificial intelligence-based surveillance systems have shown to be more accurate as well as capable of performing well in real-time scenarios, which had never been seen before. In this research, a RentinaNet model is integrated with Quantum CNN and termed as Quantum-RetinaNet. By harnessing the Quantum capabilities of QCNN, Quantum-RetinaNet strikes a balance between accuracy and speed. This innovative integration positions it as a game-changer, addressing the challenges of active monitoring in densely populated scenarios. As demand for efficient surveillance solutions continues to grow, Quantum-RetinaNet offers a compelling alternative to existing CNN models, upholding accuracy standards without sacrificing real-time performance. The unique attributes of Quantum-RetinaNet have far-reaching implications for the future of intelligent surveillance. With its enhanced processing speed, it is poised to revolutionize the field, catering to the pressing need for rapid yet precise monitoring. As Quantum-RetinaNet becomes the new standard, it ensures public safety and security while pushing the boundaries of AI in surveillance.

Diffusion models achieved great success in image synthesis, but still face challenges in high-resolution generation. Through the lens of discrete cosine transformation, we find the main reason is that \emph{the same noise level on a higher resolution results in a higher Signal-to-Noise Ratio in the frequency domain}. In this work, we present Relay Diffusion Model (RDM), which transfers a low-resolution image or noise into an equivalent high-resolution one for diffusion model via blurring diffusion and block noise. Therefore, the diffusion process can continue seamlessly in any new resolution or model without restarting from pure noise or low-resolution conditioning. RDM achieves state-of-the-art FID on CelebA-HQ and sFID on ImageNet 256$\times$256, surpassing previous works such as ADM, LDM and DiT by a large margin. All the codes and checkpoints are open-sourced at \url{https://github.com/THUDM/RelayDiffusion}.

We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce a mode orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm that is guaranteed to converge to a stationary point. By learning unique and common representations across datasets, we demonstrate perTucker's effectiveness in anomaly detection, client classification, and clustering through a simulation study and two case studies on solar flare detection and tonnage signal classification.

Joint Embedding Architecture-based self-supervised learning methods have attributed the composition of data augmentations as a crucial factor for their strong representation learning capabilities. While regional dropout strategies have proven to guide models to focus on lesser indicative parts of the objects in supervised methods, it hasn't been adopted by self-supervised methods for generating positive pairs. This is because the regional dropout methods are not suitable for the input sampling process of the self-supervised methodology. Whereas dropping informative pixels from the positive pairs can result in inefficient training, replacing patches of a specific object with a different one can steer the model from maximizing the agreement between different positive pairs. Moreover, joint embedding representation learning methods have not made robustness their primary training outcome. To this end, we propose the ViewMix augmentation policy, specially designed for self-supervised learning, upon generating different views of the same image, patches are cut and pasted from one view to another. By leveraging the different views created by this augmentation strategy, multiple joint embedding-based self-supervised methodologies obtained better localization capability and consistently outperformed their corresponding baseline methods. It is also demonstrated that incorporating ViewMix augmentation policy promotes robustness of the representations in the state-of-the-art methods. Furthermore, our experimentation and analysis of compute times suggest that ViewMix augmentation doesn't introduce any additional overhead compared to other counterparts.

Knowledge Distillation (KD) is one proposed solution to large model sizes and slow inference speed in semantic segmentation. In our research we identify 25 proposed distillation loss terms from 14 publications in the last 4 years. Unfortunately, a comparison of terms based on published results is often impossible, because of differences in training configurations. A good illustration of this problem is the comparison of two publications from 2022. Using the same models and dataset, Structural and Statistical Texture Distillation (SSTKD) reports an increase of student mIoU of 4.54 and a final performance of 29.19, while Adaptive Perspective Distillation (APD) only improves student performance by 2.06 percentage points, but achieves a final performance of 39.25. The reason for such extreme differences is often a suboptimal choice of hyperparameters and a resulting underperformance of the student model used as reference point. In our work, we reveal problems of insufficient hyperparameter tuning by showing that distillation improvements of two widely accepted frameworks, SKD and IFVD, vanish when hyperparameters are optimized sufficiently. To improve comparability of future research in the field, we establish a solid baseline for three datasets and two student models and provide extensive information on hyperparameter tuning. We find that only two out of eight techniques can compete with our simple baseline on the ADE20K dataset.

Multimodal models have gained significant success in recent years. Standard multimodal approaches often assume unchanged modalities from training stage to inference stage. In practice, however, many scenarios fail to satisfy such assumptions with missing modalities during inference, leading to limitations on where multimodal models can be applied. While existing methods mitigate the problem through reconstructing the missing modalities, it increases unnecessary computational cost, which could be just as critical, especially for large, deployed systems. To solve the problem from both sides, we propose a novel guidance network that promotes knowledge sharing during training, taking advantage of the multimodal representations to train better single-modality models for inference. Real-life experiment in violence detection shows that our proposed framework trains single-modality models that significantly outperform its traditionally trained counterparts while maintaining the same inference cost.

We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description, including numerous design decisions that are typically not widely disseminated, with a collection of studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, sensitivity to policy hyper-parameters, and choice of action space. A video demonstrating the components of the system and details of experimental results can be found at https://youtu.be/uFcnWjB42I0.

Large language models (LLMs) exhibit impressive language understanding and in-context learning abilities including natural language processing (NLP) tasks and challenging mathematical reasoning. However, due to the lack of process-supervision, applying PLMs to mathematical reasoning tasks often fail to generate correct reasoning steps and final answer even though solutions have high probabilities. To unleash the mathematical reasoning of finetuned-LLMs without any further fineutuning steps, we propose a method to endow LLMs with immediate reaction and delicate reasoning system via Monte Carlo Tree Search(MCTS) and a light energy function to rank the decision steps. In particular, We first re-formalize the finetuned-LLMs to a Residual-based Energy Model~(Residual-EBM) and apply noise contrastive estimation to estimate the parameters of energy function . Then we use MCTS with energy function as path verifier to search the output space and evaluating the reasoning path. Through extensive experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we reveal the extraordinary capabilities of our method that improve the pass@1 of the finetuned-model without further finetuning or RLHF alignment by a substantial margin.

We carefully evaluate a number of algorithms for learning in a federated environment, and test their utility for a variety of image classification tasks. We consider many issues that have not been adequately considered before: whether learning over data sets that do not have diverse sets of images affects the results; whether to use a pre-trained feature extraction "backbone"; how to evaluate learner performance (we argue that classification accuracy is not enough), among others. Overall, across a wide variety of settings, we find that vertically decomposing a neural network seems to give the best results, and outperforms more standard reconciliation-used methods.

The analysis of Synthetic Aperture Radar (SAR) imagery is an important step in remote sensing applications, and it is a challenging problem due to its inherent speckle noise. One typical solution is to model the data using the $G_I^0$ distribution and extract its roughness information, which in turn can be used in posterior imaging tasks, such as segmentation, classification and interpretation. This leads to the need of quick and reliable estimation of the roughness parameter from SAR data, especially with high resolution images. Unfortunately, traditional parameter estimation procedures are slow and prone to estimation failures. In this work, we proposed a neural network-based estimation framework that first learns how to predict underlying parameters of $G_I^0$ samples and then can be used to estimate the roughness of unseen data. We show that this approach leads to an estimator that is quicker, yields less estimation error and is less prone to failures than the traditional estimation procedures for this problem, even when we use a simple network. More importantly, we show that this same methodology can be generalized to handle image inputs and, even if trained on purely synthetic data for a few seconds, is able to perform real time pixel-wise roughness estimation for high resolution real SAR imagery.

Triplet Markov chains are general generative models for sequential data which take into account three kinds of random variables: (noisy) observations, their associated discrete labels and latent variables which aim at strengthening the distribution of the observations and their associated labels. However, in practice, we do not have at our disposal all the labels associated to the observations to estimate the parameters of such models. In this paper, we propose a general framework based on a variational Bayesian inference to train parameterized triplet Markov chain models in a semi-supervised context. The generality of our approach enables us to derive semi-supervised algorithms for a variety of generative models for sequential Bayesian classification.

Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides have been made in this field, existing methods exhibit three key limitations: (1) They often benchmark against similar model types, constraining a holistic view of performance capabilities. (2) The use of specialized synthetic and private datasets introduces biases and hampers generalizability. (3) Ambiguous evaluation measures, often tied to custom networks or downstream tasks, hinder consistent and fair comparison. To overcome these limitations, we introduce \textsf{TSGBench}, the inaugural TSG Benchmark, designed for a unified and comprehensive assessment of TSG methods. It comprises three modules: (1) a curated collection of publicly available, real-world datasets tailored for TSG, together with a standardized preprocessing pipeline; (2) a comprehensive evaluation measures suite including vanilla measures, new distance-based assessments, and visualization tools; (3) a pioneering generalization test rooted in Domain Adaptation (DA), compatible with all methods. We have conducted extensive experiments across ten real-world datasets from diverse domains, utilizing ten advanced TSG methods and twelve evaluation measures, all gauged through \textsf{TSGBench}. The results highlight its remarkable efficacy and consistency. More importantly, \textsf{TSGBench} delivers a statistical breakdown of method rankings, illuminating performance variations across different datasets and measures, and offering nuanced insights into the effectiveness of each method.

We introduce EGIC, a novel generative image compression method that allows traversing the distortion-perception curve efficiently from a single model. Specifically, we propose an implicitly encoded variant of image interpolation that predicts the residual between a MSE-optimized and GAN-optimized decoder output. On the receiver side, the user can then control the impact of the residual on the GAN-based reconstruction. Together with improved GAN-based building blocks, EGIC outperforms a wide-variety of perception-oriented and distortion-oriented baselines, including HiFiC, MRIC and DIRAC, while performing almost on par with VTM-20.0 on the distortion end. EGIC is simple to implement, very lightweight (e.g. 0.18x model parameters compared to HiFiC) and provides excellent interpolation characteristics, which makes it a promising candidate for practical applications targeting the low bit range.

Modern applications of survival analysis increasingly involve time-dependent covariates. The Python package BoXHED2.0 is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from www.github.com/BoXHED.

The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires large data with diverse styles and qualities. The diversity of data often comes from the use of various scanners of vendors. But, in practice, it is impractical to collect a sufficient amount of diverse data for training. To this end, a novel contrastive learning is developed to equip the deep learning models with better style generalization capability. Specifically, the multi-style and multi-view unsupervised self-learning scheme is carried out to seek robust feature embedding against style diversity as a pretrained model. Afterward, the pretrained network is further fine-tuned to the downstream tasks, e.g., mass detection, matching, BI-RADS rating, and breast density classification. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets. The experimental results suggest that the proposed domain generalization method can effectively improve performance of four mammographic image tasks on the data from both seen and unseen domains, and outperform many state-of-the-art (SOTA) generalization methods.

We introduce the $ARMOR_D$ methods as novel approaches to enhancing the adversarial robustness of deep learning models. These methods are based on a new class of optimal-transport-regularized divergences, constructed via an infimal convolution between an information divergence and an optimal-transport (OT) cost. We use these as tools to enhance adversarial robustness by maximizing the expected loss over a neighborhood of distributions, a technique known as distributionally robust optimization. Viewed as a tool for constructing adversarial samples, our method allows samples to be both transported, according to the OT cost, and re-weighted, according to the information divergence. We demonstrate the effectiveness of our method on malware detection and image recognition applications and find that, to our knowledge, it outperforms existing methods at enhancing the robustness against adversarial attacks. $ARMOR_D$ yields the robustified accuracy of $98.29\%$ against $FGSM$ and $98.18\%$ against $PGD^{40}$ on the MNIST dataset, reducing the error rate by more than $19.7\%$ and $37.2\%$ respectively compared to prior methods. Similarly, in malware detection, a discrete (binary) data domain, $ARMOR_D$ improves the robustified accuracy under $rFGSM^{50}$ attack compared to the previous best-performing adversarial training methods by $37.0\%$ while lowering false negative and false positive rates by $51.1\%$ and $57.53\%$, respectively.

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human intervention. Furthermore, our proposed heterogeneous model reassembly technique mitigates the adverse impact introduced by using public data with different distributions from the client data to a certain extent. Experimental results demonstrate that pFedHR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, pFedHR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner.

Estimating the effects of treatments with an associated dose on an instance's outcome, the "dose response", is relevant in a variety of domains, from healthcare to business, economics, and beyond. Such effects, also known as continuous-valued treatment effects, are typically estimated from observational data, which may be subject to dose selection bias. This means that the allocation of doses depends on pre-treatment covariates. Previous studies have shown that conventional machine learning approaches fail to learn accurate individual estimates of dose responses under the presence of dose selection bias. In this work, we propose CBRNet, a causal machine learning approach to estimate an individual dose response from observational data. CBRNet adopts the Neyman-Rubin potential outcome framework and extends the concept of balanced representation learning for overcoming selection bias to continuous-valued treatments. Our work is the first to apply representation balancing in a continuous-valued treatment setting. We evaluate our method on a newly proposed benchmark. Our experiments demonstrate CBRNet's ability to accurately learn treatment effects under selection bias and competitive performance with respect to other state-of-the-art methods.

Graph convolution is a fundamental building block for many deep neural networks on graph-structured data. In this paper, we introduce a simple, yet very effective graph convolutional network with skip connections for semi-supervised anomaly detection. The proposed layerwise propagation rule of our model is theoretically motivated by the concept of implicit fairing in geometry processing, and comprises a graph convolution module for aggregating information from immediate node neighbors and a skip connection module for combining layer-wise neighborhood representations. This propagation rule is derived from the iterative solution of the implicit fairing equation via the Jacobi method. In addition to capturing information from distant graph nodes through skip connections between the network's layers, our approach exploits both the graph structure and node features for learning discriminative node representations. These skip connections are integrated by design in our proposed network architecture. The effectiveness of our model is demonstrated through extensive experiments on five benchmark datasets, achieving better or comparable anomaly detection results against strong baseline methods. We also demonstrate through an ablation study that skip connection helps improve the model performance.

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to restart the process over again once new data becomes available. A much cheaper and more efficient solution would be to enable the continual pre-training of these models, i.e. updating pre-trained models with new data instead of re-training them from scratch. However, the distribution shift induced by novel data typically results in degraded performance on past data. Taking a step towards efficient continual pre-training, in this work, we examine the effect of different warm-up strategies. Our hypothesis is that the learning rate must be re-increased to improve compute efficiency when training on a new dataset. We study the warmup phase of models pre-trained on the Pile (upstream data, 300B tokens) as we continue to pre-train on SlimPajama (downstream data, 297B tokens), following a linear warmup and cosine decay schedule. We conduct all experiments on the Pythia 410M language model architecture and evaluate performance through validation perplexity. We experiment with different pre-training checkpoints, various maximum learning rates, and various warmup lengths. Our results show that while rewarming models first increases the loss on upstream and downstream data, in the longer run it improves the downstream performance, outperforming models trained from scratch$\unicode{x2013}$even for a large downstream dataset.

This paper introduces the Trinary decision tree, an algorithm designed to improve the handling of missing data in decision tree regressors and classifiers. Unlike other approaches, the Trinary decision tree does not assume that missing values contain any information about the response. Both theoretical calculations on estimator bias and numerical illustrations using real data sets are presented to compare its performance with established algorithms in different missing data scenarios (Missing Completely at Random (MCAR), and Informative Missingness (IM)). Notably, the Trinary tree outperforms its peers in MCAR settings, especially when data is only missing out-of-sample, while lacking behind in IM settings. A hybrid model, the TrinaryMIA tree, which combines the Trinary tree and the Missing In Attributes (MIA) approach, shows robust performance in all types of missingness. Despite the potential drawback of slower training speed, the Trinary tree offers a promising and more accurate method of handling missing data in decision tree algorithms.

We present a novel approach for efficiently generating quantum feature maps for quantum-enhanced support vector machines, a kernel-based classifier, enabling access to high-dimensional Hilbert space. Our method employs a multi-objective genetic algorithm that simultaneously maximizes classification accuracy while minimizing both the local and non-local gate costs of the quantum feature map's circuit. To achieve this, we define distinct fitness functions for local gates and entanglement gates. Comparisons with classical classifiers are given in order to understand the advantages of using quantum machine learning. Surprisingly, our experiments reveal that the optimal configuration of quantum circuits for the quantum kernel method incorporates a proportional number of non-local gates for entanglement, contrary to previous literature where non-local gates were largely suppressed. Furthermore, we demonstrate that the separability indexes of data can be effectively leveraged to determine the number of non-local gates required for the quantum support vector machine's feature maps. This insight can significantly aid in selecting appropriate parameters, such as the entanglement parameter, in various quantum programming packages like quiskit.org based on data analysis. Our findings offer valuable guidance for enhancing the efficiency and accuracy of quantum machine learning algorithms.

Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social networks. Previous efforts have applied graph neural networks (GNNs) in balanced situations where the class distribution is balanced. However, real-world data typically exhibit long-tailed class distributions, resulting in a bias towards the head classes when using GNNs and limited generalization ability over the tail classes. Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes. To address these drawbacks, we propose a novel framework called Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature extractor and an unbiased classifier in a decoupled manner. In the feature extractor training stage, we develop a graph retrieval module to search for relevant graphs that directly enrich the intra-class diversity for the tail classes. Moreover, we innovatively optimize a category-centered supervised contrastive loss to obtain discriminative representations, which is more suitable for long-tailed scenarios. In the classifier fine-tuning stage, we balance the classifier weights with two weight regularization techniques, i.e., Max-norm and weight decay. Experiments on various popular benchmarks verify the superiority of the proposed method against state-of-the-art approaches.

Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to be robust against uncertainties in ground conditions. However, it is still not fully understood how the nervous system resolves the musculoskeletal redundancy to solve the multi-objective control problem considering stability, robustness, and energy efficiency. In computer simulations, energy minimization has been shown to be a successful optimization target, reproducing natural walking with trajectory optimization or reflex-based control methods. However, these methods focus on particular motions at a time and the resulting controllers are limited when compensating for perturbations. In robotics, reinforcement learning~(RL) methods recently achieved highly stable (and efficient) locomotion on quadruped systems, but the generation of human-like walking with bipedal biomechanical models has required extensive use of expert data sets. This strong reliance on demonstrations often results in brittle policies and limits the application to new behaviors, especially considering the potential variety of movements for high-dimensional musculoskeletal models in 3D. Achieving natural locomotion with RL without sacrificing its incredible robustness might pave the way for a novel approach to studying human walking in complex natural environments. Videos: https://sites.google.com/view/naturalwalkingrl

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res, https://huggingface.co/datasets/LEAP/ClimSim_low-res, and https://huggingface.co/datasets/LEAP/ClimSim_low-res_aqua-planet) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

Attention mechanism is a central component of the transformer architecture which led to the phenomenal success of large language models. However, the theoretical principles underlying the attention mechanism are poorly understood, especially its nonconvex optimization dynamics. In this work, we explore the seminal softmax-attention model $f(\boldsymbol{X})=\langle \boldsymbol{Xv}, \texttt{softmax}(\boldsymbol{XWp})\rangle$, where $\boldsymbol{X}$ is the token sequence and $(\boldsymbol{v},\boldsymbol{W},\boldsymbol{p})$ are trainable parameters. We prove that running gradient descent on $\boldsymbol{p}$, or equivalently $\boldsymbol{W}$, converges in direction to a max-margin solution that separates $\textit{locally-optimal}$ tokens from non-optimal ones. This clearly formalizes attention as an optimal token selection mechanism. Remarkably, our results are applicable to general data and precisely characterize $\textit{optimality}$ of tokens in terms of the value embeddings $\boldsymbol{Xv}$ and problem geometry. We also provide a broader regularization path analysis that establishes the margin maximizing nature of attention even for nonlinear prediction heads. When optimizing $\boldsymbol{v}$ and $\boldsymbol{p}$ simultaneously with logistic loss, we identify conditions under which the regularization paths directionally converge to their respective hard-margin SVM solutions where $\boldsymbol{v}$ separates the input features based on their labels. Interestingly, the SVM formulation of $\boldsymbol{p}$ is influenced by the support vector geometry of $\boldsymbol{v}$. Finally, we verify our theoretical findings via numerical experiments and provide insights.

Recent works have demonstrated that the sample complexity of gradient-based learning of single index models, i.e. functions that depend on a 1-dimensional projection of the input data, is governed by their information exponent. However, these results are only concerned with isotropic data, while in practice the input often contains additional structure which can implicitly guide the algorithm. In this work, we investigate the effect of a spiked covariance structure and reveal several interesting phenomena. First, we show that in the anisotropic setting, the commonly used spherical gradient dynamics may fail to recover the true direction, even when the spike is perfectly aligned with the target direction. Next, we show that appropriate weight normalization that is reminiscent of batch normalization can alleviate this issue. Further, by exploiting the alignment between the (spiked) input covariance and the target, we obtain improved sample complexity compared to the isotropic case. In particular, under the spiked model with a suitably large spike, the sample complexity of gradient-based training can be made independent of the information exponent while also outperforming lower bounds for rotationally invariant kernel methods.

The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines. However, traditional numerical solvers are limited by the choice of step sizes when estimating integration, resulting in a trade-off between accuracy and computational efficiency. To address this challenge, we introduce a deep learning-based corrector called Neural Vector (NeurVec), which can compensate for integration errors and enable larger time step sizes in simulations. Our extensive experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability on a continuous phase space, even when trained using limited and discrete data. NeurVec significantly accelerates traditional solvers, achieving speeds tens to hundreds of times faster while maintaining high levels of accuracy and stability. Moreover, NeurVec's simple-yet-effective design, combined with its ease of implementation, has the potential to establish a new paradigm for fast-solving differential equations based on deep learning.

Over the last decades, Stochastic Gradient Descent (SGD) has been intensively studied by the Machine Learning community. Despite its versatility and excellent performance, the optimization of large models via SGD still is a time-consuming task. To reduce training time, it is common to distribute the training process across multiple devices. Recently, it has been shown that the convergence of asynchronous SGD (ASGD) will always be faster than mini-batch SGD. However, despite these improvements in the theoretical bounds, most ASGD convergence-rate proofs still rely on a centralized parameter server, which is prone to become a bottleneck when scaling out the gradient computations across many distributed processes. In this paper, we present a novel convergence-rate analysis for decentralized and asynchronous SGD (DASGD) which does not require partial synchronization among nodes nor restrictive network topologies. Specifically, we provide a bound of $\mathcal{O}(\sigma\epsilon^{-2}) + \mathcal{O}(QS_{avg}\epsilon^{-3/2}) + \mathcal{O}(S_{avg}\epsilon^{-1})$ for the convergence rate of DASGD, where $S_{avg}$ is the average staleness between models, $Q$ is a constant that bounds the norm of the gradients, and $\epsilon$ is a (small) error that is allowed within the bound. Furthermore, when gradients are not bounded, we prove the convergence rate of DASGD to be $\mathcal{O}(\sigma\epsilon^{-2}) + \mathcal{O}(\sqrt{\hat{S}_{avg}\hat{S}_{max}}\epsilon^{-1})$, with $\hat{S}_{max}$ and $\hat{S}_{avg}$ representing a loose version of the average and maximum staleness, respectively. Our convergence proof holds for a fixed stepsize and any non-convex, homogeneous, and L-smooth objective function. We anticipate that our results will be of high relevance for the adoption of DASGD by a broad community of researchers and developers.

This paper explores the expressive power of deep neural networks for a diverse range of activation functions. An activation function set $\mathscr{A}$ is defined to encompass the majority of commonly used activation functions, such as $\mathtt{ReLU}$, $\mathtt{LeakyReLU}$, $\mathtt{ReLU}^2$, $\mathtt{ELU}$, $\mathtt{SELU}$, $\mathtt{Softplus}$, $\mathtt{GELU}$, $\mathtt{SiLU}$, $\mathtt{Swish}$, $\mathtt{Mish}$, $\mathtt{Sigmoid}$, $\mathtt{Tanh}$, $\mathtt{Arctan}$, $\mathtt{Softsign}$, $\mathtt{dSiLU}$, and $\mathtt{SRS}$. We demonstrate that for any activation function $\varrho\in \mathscr{A}$, a $\mathtt{ReLU}$ network of width $N$ and depth $L$ can be approximated to arbitrary precision by a $\varrho$-activated network of width $4N$ and depth $2L$ on any bounded set. This finding enables the extension of most approximation results achieved with $\mathtt{ReLU}$ networks to a wide variety of other activation functions, at the cost of slightly larger constants.

Despite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i.e., generating content that deviates from facts seen during pretraining. We propose a simple decoding strategy for reducing hallucinations with pretrained LLMs that does not require conditioning on retrieved external knowledge nor additional fine-tuning. Our approach obtains the next-token distribution by contrasting the differences in logits obtained from projecting the later layers versus earlier layers to the vocabulary space, exploiting the fact that factual knowledge in an LLMs has generally been shown to be localized to particular transformer layers. We find that this Decoding by Contrasting Layers (DoLa) approach is able to better surface factual knowledge and reduce the generation of incorrect facts. DoLa consistently improves the truthfulness across multiple choices tasks and open-ended generation tasks, for example improving the performance of LLaMA family models on TruthfulQA by 12-17% absolute points, demonstrating its potential in making LLMs reliably generate truthful facts.

In recent years, understanding the implicit regularization of neural networks (NNs) has become a central task in deep learning theory. However, implicit regularization is itself not completely defined and well understood. In this work, we attempt to mathematically define and study implicit regularization. Importantly, we explore the limitations of a common approach to characterizing implicit regularization using data-independent functions. We propose two dynamical mechanisms, i.e., Two-point and One-point Overlapping mechanisms, based on which we provide two recipes for producing classes of one-hidden-neuron NNs that provably cannot be fully characterized by a type of or all data-independent functions. Following the previous works, our results further emphasize the profound data dependency of implicit regularization in general, inspiring us to study in detail the data dependency of NN implicit regularization in the future.

Interpolators are unstable. For example, the mininum $\ell_2$ norm least square interpolator exhibits unbounded test errors when dealing with noisy data. In this paper, we study how ensemble stabilizes and thus improves the generalization performance, measured by the out-of-sample prediction risk, of an individual interpolator. We focus on bagged linear interpolators, as bagging is a popular randomization-based ensemble method that can be implemented in parallel. We introduce the multiplier-bootstrap-based bagged least square estimator, which can then be formulated as an average of the sketched least square estimators. The proposed multiplier bootstrap encompasses the classical bootstrap with replacement as a special case, along with a more intriguing variant which we call the Bernoulli bootstrap. Focusing on the proportional regime where the sample size scales proportionally with the feature dimensionality, we investigate the out-of-sample prediction risks of the sketched and bagged least square estimators in both underparametrized and overparameterized regimes. Our results reveal the statistical roles of sketching and bagging. In particular, sketching modifies the aspect ratio and shifts the interpolation threshold of the minimum $\ell_2$ norm estimator. However, the risk of the sketched estimator continues to be unbounded around the interpolation threshold due to excessive variance. In stark contrast, bagging effectively mitigates this variance, leading to a bounded limiting out-of-sample prediction risk. To further understand this stability improvement property, we establish that bagging acts as a form of implicit regularization, substantiated by the equivalence of the bagged estimator with its explicitly regularized counterpart. We also discuss several extensions.

Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and achieved state-of-the-art performance. However, besides being effective, in practice, the practitioners would further like to know what causes the abnormal outcome and how to further fix it. In this work, we propose RootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigation from a causal perspective. Especially, we formulate anomalies caused by external interventions on the normal causal mechanism and aim to locate the abnormal features with external interventions as root causes. After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal. Experiments on three datasets show that our approach can locate the root causes and further flip the abnormal labels.

The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (>87%). Although our results are preliminary, they indicate that RS and topological analysis together may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps will include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to understand if topological data analysis could support the characterization of AD subtypes.

Deep neural networks employing error back-propagation for learning can suffer from exploding and vanishing gradient problems. Numerous solutions have been proposed such as normalisation techniques or limiting activation functions to linear rectifying units. In this work we follow a different approach which is particularly applicable to closed-loop learning of forward models where back-propagation makes exclusive use of the sign of the error signal to prime the learning, whilst a global relevance signal modulates the rate of learning. This is inspired by the interaction between local plasticity and a global neuromodulation. For example, whilst driving on an empty road, one can allow for slow step-wise optimisation of actions, whereas, at a busy junction, an error must be corrected at once. Hence, the error is the priming signal and the intensity of the experience is a modulating factor in the weight change. The advantages of this Prime and Modulate paradigm is twofold: it is free from normalisation and it makes use of relevant cues from the environment to enrich the learning. We present a mathematical derivation of the learning rule in z-space and demonstrate the real-time performance with a robotic platform. The results show a significant improvement in the speed of convergence compared to that of the conventional back-propagation.

The exclusive or (xor) function is one of the simplest examples that illustrate why nonlinear feedforward networks are superior to linear regression for machine learning applications. We review the xor representation and approximation problems and discuss their solutions in terms of probabilistic logic and associative copula functions. After briefly reviewing the specification of feedforward networks, we compare the dynamics of learned error surfaces with different activation functions such as RELU and tanh through a set of colorful three-dimensional charts. The copula representations extend xor from Boolean to real values, thereby providing a convenient way to demonstrate the concept of cross-validation on in-sample and out-sample data sets. Our approach is pedagogical and is meant to be a machine learning prolegomenon.

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as $\mathcal{O}(T^2 \times \text{polylog}(n))$, where $n$ is the size of the models and $T$ is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, has been recently extended to exploit prior knowledge on the cardinality of each cluster. Such knowledge is used to increase performance as well as solution quality. In this paper, we propose a global optimization approach based on the branch-and-cut technique to solve the cardinality-constrained MSSC. For the lower bound routine, we use the semidefinite programming (SDP) relaxation recently proposed by Rujeerapaiboon et al. [SIAM J. Optim. 29(2), 1211-1239, (2019)]. However, this relaxation can be used in a branch-and-cut method only for small-size instances. Therefore, we derive a new SDP relaxation that scales better with the instance size and the number of clusters. In both cases, we strengthen the bound by adding polyhedral cuts. Benefiting from a tailored branching strategy which enforces pairwise constraints, we reduce the complexity of the problems arising in the children nodes. For the upper bound, instead, we present a local search procedure that exploits the solution of the SDP relaxation solved at each node. Computational results show that the proposed algorithm globally solves, for the first time, real-world instances of size 10 times larger than those solved by state-of-the-art exact methods.

Editing a local region or a specific object in a 3D scene represented by a NeRF or consistently blending a new realistic object into the scene is challenging, mainly due to the implicit nature of the scene representation. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

With the overwhelming trend of mask image modeling led by MAE, generative pre-training has shown a remarkable potential to boost the performance of fundamental models in 2D vision. However, in 3D vision, the over-reliance on Transformer-based backbones and the unordered nature of point clouds have restricted the further development of generative pre-training. In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model. We propose to generate view images from different instructed poses via the cross-attention mechanism as the pre-training scheme. Generating view images has more precise supervision than its point cloud counterpart, thus assisting 3D backbones to have a finer comprehension of the geometrical structure and stereoscopic relations of the point cloud. Experimental results have proved the superiority of our proposed 3D-to-2D generative pre-training over previous pre-training methods. Our method is also effective in boosting the performance of architecture-oriented approaches, achieving state-of-the-art performance when fine-tuning on ScanObjectNN classification and ShapeNetPart segmentation tasks. Code is available at https://github.com/wangzy22/TAP.

Adaptive interfaces can help users perform sequential decision-making tasks like robotic teleoperation given noisy, high-dimensional command signals (e.g., from a brain-computer interface). Recent advances in human-in-the-loop machine learning enable such systems to improve by interacting with users, but tend to be limited by the amount of data that they can collect from individual users in practice. In this paper, we propose a reinforcement learning algorithm to address this by training an interface to map raw command signals to actions using a combination of offline pre-training and online fine-tuning. To address the challenges posed by noisy command signals and sparse rewards, we develop a novel method for representing and inferring the user's long-term intent for a given trajectory. We primarily evaluate our method's ability to assist users who can only communicate through noisy, high-dimensional input channels through a user study in which 12 participants performed a simulated navigation task by using their eye gaze to modulate a 128-dimensional command signal from their webcam. The results show that our method enables successful goal navigation more often than a baseline directional interface, by learning to denoise user commands signals and provide shared autonomy assistance. We further evaluate on a simulated Sawyer pushing task with eye gaze control, and the Lunar Lander game with simulated user commands, and find that our method improves over baseline interfaces in these domains as well. Extensive ablation experiments with simulated user commands empirically motivate each component of our method.

We present a novel human body model formulated by an extensive set of anthropocentric measurements, which is capable of generating a wide range of human body shapes and poses. The proposed model enables direct modeling of specific human identities through a deep generative architecture, which can produce humans in any arbitrary pose. It is the first of its kind to have been trained end-to-end using only synthetically generated data, which not only provides highly accurate human mesh representations but also allows for precise anthropometry of the body. Moreover, using a highly diverse animation library, we articulated our synthetic humans' body and hands to maximize the diversity of the learnable priors for model training. Our model was trained on a dataset of $100k$ procedurally-generated posed human meshes and their corresponding anthropometric measurements. Our synthetic data generator can be used to generate millions of unique human identities and poses for non-commercial academic research purposes.

Weakly Supervised Semantic Segmentation (WSSS) relying only on image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for generating a large number of pixel-wise masks in a given dataset. However, most state-of-the-art image-level WSSS techniques lack an understanding of the geometric features embedded in the images since the network cannot derive any object boundary information from just image-level labels. We define a boundary here as the line separating an object and its background, or two different objects. To address this drawback, we are proposing our novel ReFit framework, which deploys state-of-the-art class activation maps combined with various post-processing techniques in order to achieve fine-grained higher-accuracy segmentation masks. To achieve this, we investigate a state-of-the-art unsupervised segmentation network that can be used to construct a boundary map, which enables ReFit to predict object locations with sharper boundaries. By applying our method to WSSS predictions, we achieved up to 10% improvement over the current state-of-the-art WSSS methods for medical imaging. The framework is open-source, to ensure that our results are reproducible, and accessible online at https://github.com/bharathprabakaran/ReFit.

Generative Adversarial Networks (GANs) can produce high-quality samples, but do not provide an estimate of the probability density around the samples. However, it has been noted that maximizing the log-likelihood within an energy-based setting can lead to an adversarial framework where the discriminator provides unnormalized density (often called energy). We further develop this perspective, incorporate importance sampling, and show that 1) Wasserstein GAN performs a biased estimate of the partition function, and we propose instead to use an unbiased estimator; and 2) when optimizing for likelihood, one must maximize generator entropy. This is hypothesized to provide a better mode coverage. Different from previous works, we explicitly compute the density of the generated samples. This is the key enabler to designing an unbiased estimator of the partition function and computation of the generator entropy term. The generator density is obtained via a new type of flow network, called one-way flow network, that is less constrained in terms of architecture, as it does not require a tractable inverse function. Our experimental results show that our method converges faster, produces comparable sample quality to GANs with similar architecture, successfully avoids over-fitting to commonly used datasets and produces smooth low-dimensional latent representations of the training data.

We address the problem of identifying the optimal policy with a fixed confidence level in a multi-armed bandit setup, when \emph{the arms are subject to linear constraints}. Unlike the standard best-arm identification problem which is well studied, the optimal policy in this case may not be deterministic and could mix between several arms. This changes the geometry of the problem which we characterize via an information-theoretic lower bound. We introduce two asymptotically optimal algorithms for this setting, one based on the Track-and-Stop method and the other based on a game-theoretic approach. Both these algorithms try to track an optimal allocation based on the lower bound and computed by a weighted projection onto the boundary of a normal cone. Finally, we provide empirical results that validate our bounds and visualize how constraints change the hardness of the problem.

Distribution shifts are all too common in real-world applications of machine learning. Domain adaptation (DA) aims to address this by providing various frameworks for adapting models to the deployment data without using labels. However, the domain shift scenario raises a second more subtle challenge: the difficulty of performing hyperparameter optimisation (HPO) for these adaptation algorithms without access to a labelled validation set. The unclear validation protocol for DA has led to bad practices in the literature, such as performing HPO using the target test labels when, in real-world scenarios, they are not available. This has resulted in over-optimism about DA research progress compared to reality. In this paper, we analyse the state of DA when using good evaluation practice, by benchmarking a suite of candidate validation criteria and using them to assess popular adaptation algorithms. We show that there are challenges across all three branches of domain adaptation methodology including Unsupervised Domain Adaptation (UDA), Source-Free Domain Adaptation (SFDA), and Test Time Adaptation (TTA). While the results show that realistically achievable performance is often worse than expected, they also show that using proper validation splits is beneficial, as well as showing that some previously unexplored validation metrics provide the best options to date. Altogether, our improved practices covering data, training, validation and hyperparameter optimisation form a new rigorous pipeline to improve benchmarking, and hence research progress, within this important field going forward.

This paper presents a bonobo detection and classification pipeline built from the commonly used machine learning methods. Such application is motivated by the need to test bonobos in their enclosure using touch screen devices without human assistance. This work introduces a newly acquired dataset based on bonobo recordings generated semi-automatically. The recordings are weakly labelled and fed to a macaque detector in order to spatially detect the individual present in the video. Handcrafted features coupled with different classification algorithms and deep-learning methods using a ResNet architecture are investigated for bonobo identification. Performance is compared in terms of classification accuracy on the splits of the database using different data separation methods. We demonstrate the importance of data preparation and how a wrong data separation can lead to false good results. Finally, after a meaningful separation of the data, the best classification performance is obtained using a fine-tuned ResNet model and reaches 75% of accuracy.

Safety is an essential asset when learning control policies for physical systems, as violating safety constraints during training can lead to expensive hardware damage. In response to this need, the field of safe learning has emerged with algorithms that can provide probabilistic safety guarantees without knowledge of the underlying system dynamics. Those algorithms often rely on Gaussian process inference. Unfortunately, Gaussian process inference scales cubically with the number of data points, limiting applicability to high-dimensional and embedded systems. In this paper, we propose a safe learning algorithm that provides probabilistic safety guarantees but leverages the Nadaraya-Watson estimator instead of Gaussian processes. For the Nadaraya-Watson estimator, we can reach logarithmic scaling with the number of data points. We provide theoretical guarantees for the estimates, embed them into a safe learning algorithm, and show numerical experiments on a simulated seven-degrees-of-freedom robot manipulator.

This work investigates the nuanced algorithm design choices for deep learning in the presence of computational-statistical gaps. We begin by considering offline sparse parity learning, a supervised classification problem which admits a statistical query lower bound for gradient-based training of a multilayer perceptron. This lower bound can be interpreted as a multi-resource tradeoff frontier: successful learning can only occur if one is sufficiently rich (large model), knowledgeable (large dataset), patient (many training iterations), or lucky (many random guesses). We show, theoretically and experimentally, that sparse initialization and increasing network width yield significant improvements in sample efficiency in this setting. Here, width plays the role of parallel search: it amplifies the probability of finding "lottery ticket" neurons, which learn sparse features more sample-efficiently. Finally, we show that the synthetic sparse parity task can be useful as a proxy for real problems requiring axis-aligned feature learning. We demonstrate improved sample efficiency on tabular classification benchmarks by using wide, sparsely-initialized MLP models; these networks sometimes outperform tuned random forests.

Small devices are frequently used in IoT and smart-city applications to perform periodic dedicated tasks with soft deadlines. This work focuses on developing methods to derive efficient power-management methods for periodic tasks on small devices. We first study the limitations of the existing Linux built-in methods used in small devices. We illustrate three typical workload/system patterns that are challenging to manage with Linux's built-in solutions. We develop a reinforcement-learning-based technique with temporal encoding to derive an effective DVFS governor even with the presence of the three system patterns. The derived governor uses only one performance counter, the same as the built-in Linux mechanism, and does not require an explicit task model for the workload. We implemented a prototype system on the Nvidia Jetson Nano Board and experimented with it with six applications, including two self-designed and four benchmark applications. Under different deadline constraints, our approach can quickly derive a DVFS governor that can adapt to performance requirements and outperform the built-in Linux approach in energy saving. On Mibench workloads, with performance slack ranging from 0.04 s to 0.4 s, the proposed method can save 3% - 11% more energy compared to Ondemand. AudioReg and FaceReg applications tested have 5%- 14% energy-saving improvement. We have open-sourced the implementation of our in-kernel quantized neural network engine. The codebase can be found at: https://github.com/coladog/tinyagent.

Lithium-ion (Li-ion) batteries are the primary power source in various applications due to their high energy and power density. Their market was estimated to be up to 48 billion U.S. dollars in 2022. However, the widespread adoption of Li-ion batteries has resulted in counterfeit cell production, which can pose safety hazards to users. Counterfeit cells can cause explosions or fires, and their prevalence in the market makes it difficult for users to detect fake cells. Indeed, current battery authentication methods can be susceptible to advanced counterfeiting techniques and are often not adaptable to various cells and systems. In this paper, we improve the state of the art on battery authentication by proposing two novel methodologies, DCAuth and EISthentication, which leverage the internal characteristics of each cell through Machine Learning models. Our methods automatically authenticate lithium-ion battery models and architectures using data from their regular usage without the need for any external device. They are also resilient to the most common and critical counterfeit practices and can scale to several batteries and devices. To evaluate the effectiveness of our proposed methodologies, we analyze time-series data from a total of 20 datasets that we have processed to extract meaningful features for our analysis. Our methods achieve high accuracy in battery authentication for both architectures (up to 0.99) and models (up to 0.96). Moreover, our methods offer comparable identification performances. By using our proposed methodologies, manufacturers can ensure that devices only use legitimate batteries, guaranteeing the operational state of any system and safety measures for the users.

Temporal Knowledge Graph (TKG) is an extension of traditional Knowledge Graph (KG) that incorporates the dimension of time. Reasoning on TKGs is a crucial task that aims to predict future facts based on historical occurrences. The key challenge lies in uncovering structural dependencies within historical subgraphs and temporal patterns. Most existing approaches model TKGs relying on entity modeling, as nodes in the graph play a crucial role in knowledge representation. However, the real-world scenario often involves an extensive number of entities, with new entities emerging over time. This makes it challenging for entity-dependent methods to cope with extensive volumes of entities, and effectively handling newly emerging entities also becomes a significant challenge. Therefore, we propose Temporal Inductive Path Neural Network (TiPNN), which models historical information in an entity-independent perspective. Specifically, TiPNN adopts a unified graph, namely history temporal graph, to comprehensively capture and encapsulate information from history. Subsequently, we utilize the defined query-aware temporal paths to model historical path information related to queries on history temporal graph for the reasoning. Extensive experiments illustrate that the proposed model not only attains significant performance enhancements but also handles inductive settings, while additionally facilitating the provision of reasoning evidence through history temporal graphs.

Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for the tracking and diagnosis of progression. However, existing vessel segmentation methods that heavily rely on encoder-decoder structures struggle to capture contextual information about retinal vessel configurations, leading to challenges in reconciling semantic disparities between encoder and decoder features. To address this, we propose a novel feature enhancement segmentation network (FES-Net) that achieves accurate pixel-wise segmentation without requiring additional image enhancement steps. FES-Net directly processes the input image and utilizes four prompt convolutional blocks (PCBs) during downsampling, complemented by a shallow upsampling approach to generate a binary mask for each class. We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF. The evaluation results clearly demonstrate the superior performance of FES-Net compared to other competitive approaches documented in the existing literature.

Patch robustness certification ensures no patch within a given bound on a sample can manipulate a deep learning model to predict a different label. However, existing techniques cannot certify samples that cannot meet their strict bars at the classifier or patch region levels. This paper proposes MajorCert. MajorCert firstly finds all possible label sets manipulatable by the same patch region on the same sample across the underlying classifiers, then enumerates their combinations element-wise, and finally checks whether the majority invariant of all these combinations is intact to certify samples.

Advances in Semi-Supervised Learning (SSL) have almost entirely closed the gap between SSL and Supervised Learning at a fraction of the number of labels. However, recent performance improvements have often come \textit{at the cost of significantly increased training computation}. To address this, we propose Curriculum Batch Size (CBS), \textit{an unlabeled batch size curriculum which exploits the natural training dynamics of deep neural networks.} A small unlabeled batch size is used in the beginning of training and is gradually increased to the end of training. A fixed curriculum is used regardless of dataset, model or number of epochs, and reduced training computations is demonstrated on all settings. We apply CBS, strong labeled augmentation, Curriculum Pseudo Labeling (CPL) \citep{FlexMatch} to FixMatch \citep{FixMatch} and term the new SSL algorithm Fast FixMatch. We perform an ablation study to show that strong labeled augmentation and/or CPL do not significantly reduce training computations, but, in synergy with CBS, they achieve optimal performance. Fast FixMatch also achieves substantially higher data utilization compared to previous state-of-the-art. Fast FixMatch achieves between $2.1\times$ - $3.4\times$ reduced training computations on CIFAR-10 with all but 40, 250 and 4000 labels removed, compared to vanilla FixMatch, while attaining the same cited state-of-the-art error rate \citep{FixMatch}. Similar results are achieved for CIFAR-100, SVHN and STL-10. Finally, Fast MixMatch achieves between $2.6\times$ - $3.3\times$ reduced training computations in federated SSL tasks and online/streaming learning SSL tasks, which further demonstrate the generializbility of Fast MixMatch to different scenarios and tasks.

Measuring distance or similarity between time-series data is a fundamental aspect of many applications including classification and clustering. Existing measures may fail to capture similarities due to local trends (shapes) and may even produce misleading results. Our goal is to develop a measure that looks for similar trends occurring around similar times and is easily interpretable for researchers in applied domains. This is particularly useful for applications where time-series have a sequence of meaningful local trends that are ordered, such as in epidemics (a surge to an increase to a peak to a decrease). We propose a novel measure, DTW+S, which creates an interpretable "closeness-preserving" matrix representation of the time-series, where each column represents local trends, and then it applies Dynamic Time Warping to compute distances between these matrices. We present a theoretical analysis that supports the choice of this representation. We demonstrate the utility of DTW+S in ensemble building and clustering of epidemic curves. We also demonstrate that our approach results in better classification compared to Dynamic Time Warping for a class of datasets, particularly when local trends rather than scale play a decisive role.

In this work, we introduce GraPhSyM, a Graph Attention Network (GATv2) model for fast and accurate estimation of post-physical synthesis circuit delay and area metrics from pre-physical synthesis circuit netlists. Once trained, GraPhSyM provides accurate visibility of final design metrics to early EDA stages, such as logic synthesis, without running the slow physical synthesis flow, enabling global co-optimization across stages. Additionally, the swift and precise feedback provided by GraPhSyM is instrumental for machine-learning-based EDA optimization frameworks. Given a gate-level netlist of a circuit represented as a graph, GraPhSyM utilizes graph structure, connectivity, and electrical property features to predict the impact of physical synthesis transformations such as buffer insertion and gate sizing. When trained on a dataset of 6000 prefix adder designs synthesized at an aggressive delay target, GraPhSyM can accurately predict the post-synthesis delay (98.3%) and area (96.1%) metrics of unseen adders with a fast 0.22s inference time. Furthermore, we illustrate the compositionality of GraPhSyM by employing the model trained on a fixed delay target to accurately anticipate post-synthesis metrics at a variety of unseen delay targets. Lastly, we report promising generalization capabilities of the GraPhSyM model when it is evaluated on circuits different from the adders it was exclusively trained on. The results show the potential for GraPhSyM to serve as a powerful tool for advanced optimization techniques and as an oracle for EDA machine learning frameworks.

A common setting in multitask reinforcement learning (RL) demands that an agent rapidly adapt to various stationary reward functions randomly sampled from a fixed distribution. In such situations, the successor representation (SR) is a popular framework which supports rapid policy evaluation by decoupling a policy's expected discounted, cumulative state occupancies from a specific reward function. However, in the natural world, sequential tasks are rarely independent, and instead reflect shifting priorities based on the availability and subjective perception of rewarding stimuli. Reflecting this disjunction, in this paper we study the phenomenon of diminishing marginal utility and introduce a novel state representation, the $\lambda$ representation ($\lambda$R) which, surprisingly, is required for policy evaluation in this setting and which generalizes the SR as well as several other state representations from the literature. We establish the $\lambda$R's formal properties and examine its normative advantages in the context of machine learning, as well as its usefulness for studying natural behaviors, particularly foraging.

The ability to predict the future trajectory of a patient is a key step toward the development of therapeutics for complex diseases such as Alzheimer's disease (AD). However, most machine learning approaches developed for prediction of disease progression are either single-task or single-modality models, which can not be directly adopted to our setting involving multi-task learning with high dimensional images. Moreover, most of those approaches are trained on a single dataset (i.e. cohort), which can not be generalized to other cohorts. We propose a novel multimodal multi-task deep learning model to predict AD progression by analyzing longitudinal clinical and neuroimaging data from multiple cohorts. Our proposed model integrates high dimensional MRI features from a 3D convolutional neural network with other data modalities, including clinical and demographic information, to predict the future trajectory of patients. Our model employs an adversarial loss to alleviate the study-specific imaging bias, in particular the inter-study domain shifts. In addition, a Sharpness-Aware Minimization (SAM) optimization technique is applied to further improve model generalization. The proposed model is trained and tested on various datasets in order to evaluate and validate the results. Our results showed that 1) our model yields significant improvement over the baseline models, and 2) models using extracted neuroimaging features from 3D convolutional neural network outperform the same models when applied to MRI-derived volumetric features.

We study automated intrusion response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed stochastic game. To solve the game we follow an approach where attack and defense strategies co-evolve through reinforcement learning and self-play toward an equilibrium. Solutions proposed in previous work prove the feasibility of this approach for small infrastructures but do not scale to realistic scenarios due to the exponential growth in computational complexity with the infrastructure size. We address this problem by introducing a method that recursively decomposes the game into subgames which can be solved in parallel. Applying optimal stopping theory we show that the best response strategies in these subgames exhibit threshold structures, which allows us to compute them efficiently. To solve the decomposed game we introduce an algorithm called Decompositional Fictitious Self-Play (DFSP), which learns Nash equilibria through stochastic approximation. We evaluate the learned strategies in an emulation environment where real intrusions and response actions can be executed. The results show that the learned strategies approximate an equilibrium and that DFSP significantly outperforms a state-of-the-art algorithm for a realistic infrastructure configuration.

Emotion recognition is a complex task due to the inherent subjectivity in both the perception and production of emotions. The subjectivity of emotions poses significant challenges in developing accurate and robust computational models. This thesis examines critical facets of emotion recognition, beginning with the collection of diverse datasets that account for psychological factors in emotion production. To handle the challenge of non-representative training data, this work collects the Multimodal Stressed Emotion dataset, which introduces controlled stressors during data collection to better represent real-world influences on emotion production. To address issues with label subjectivity, this research comprehensively analyzes how data augmentation techniques and annotation schemes impact emotion perception and annotator labels. It further handles natural confounding variables and variations by employing adversarial networks to isolate key factors like stress from learned emotion representations during model training. For tackling concerns about leakage of sensitive demographic variables, this work leverages adversarial learning to strip sensitive demographic information from multimodal encodings. Additionally, it proposes optimized sociological evaluation metrics aligned with cost-effective, real-world needs for model testing. This research advances robust, practical emotion recognition through multifaceted studies of challenges in datasets, labels, modeling, demographic and membership variable encoding in representations, and evaluation. The groundwork has been laid for cost-effective, generalizable emotion recognition models that are less likely to encode sensitive demographic information.

Cinematic audio source separation is a relatively new subtask of audio source separation, with the aim of extracting the dialogue stem, the music stem, and the effects stem from their mixture. In this work, we developed a model generalizing the Bandsplit RNN for any complete or overcomplete partitions of the frequency axis. Psycho-acoustically motivated frequency scales were used to inform the band definitions which are now defined with redundancy for more reliable feature extraction. A loss function motivated by the signal-to-noise ratio and the sparsity-promoting property of the 1-norm was proposed. We additionally exploit the information-sharing property of a common-encoder setup to reduce computational complexity during both training and inference, improve separation performance for hard-to-generalize classes of sounds, and allow flexibility during inference time with easily detachable decoders. Our best model sets the state of the art on the Divide and Remaster dataset with performance above the ideal ratio mask for the dialogue stem.

Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to prognostically predict clinically important events based on patient data. However, existing approaches often have limitations; some focus only on ranking patients by survivability, neglecting to estimate the actual event time, while others treat the problem as a classification task, ignoring the inherent time-ordered structure of the events. Furthermore, the effective utilization of censored samples - training data points where the exact event time is unknown - is essential for improving the predictive accuracy of the model. In this paper, we introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data. Furthermore, CenTime is easily integrated with deep learning models with no restrictions on batch size or the number of uncensored samples. We compare our approach with standard survival analysis methods, including the Cox proportional-hazard model and DeepHit. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance. Our implementation is publicly available at https://github.com/ahmedhshahin/CenTime.

Causal inference plays a vital role in diverse domains like epidemiology, healthcare, and economics. De-confounding and counterfactual prediction in observational data has emerged as a prominent concern in causal inference research. While existing models tackle observed confounders, the presence of unobserved confounders remains a significant challenge, distorting causal inference and impacting counterfactual outcome accuracy. To address this, we propose a novel variational learning model of unobserved confounders for counterfactual inference (VLUCI), which generates the posterior distribution of unobserved confounders. VLUCI relaxes the unconfoundedness assumption often overlooked by most causal inference methods. By disentangling observed and unobserved confounders, VLUCI constructs a doubly variational inference model to approximate the distribution of unobserved confounders, which are used for inferring more accurate counterfactual outcomes. Extensive experiments on synthetic and semi-synthetic datasets demonstrate VLUCI's superior performance in inferring unobserved confounders. It is compatible with state-of-the-art counterfactual inference models, significantly improving inference accuracy at both group and individual levels. Additionally, VLUCI provides confidence intervals for counterfactual outcomes, aiding decision-making in risk-sensitive domains. We further clarify the considerations when applying VLUCI to cases where unobserved confounders don't strictly conform to our model assumptions using the public IHDP dataset as an example, highlighting the practical advantages of VLUCI.

We present a novel approach to single-view face relighting in the wild. Handling non-diffuse effects, such as global illumination or cast shadows, has long been a challenge in face relighting. Prior work often assumes Lambertian surfaces, simplified lighting models or involves estimating 3D shape, albedo, or a shadow map. This estimation, however, is error-prone and requires many training examples with lighting ground truth to generalize well. Our work bypasses the need for accurate estimation of intrinsic components and can be trained solely on 2D images without any light stage data, multi-view images, or lighting ground truth. Our key idea is to leverage a conditional diffusion implicit model (DDIM) for decoding a disentangled light encoding along with other encodings related to 3D shape and facial identity inferred from off-the-shelf estimators. We also propose a novel conditioning technique that eases the modeling of the complex interaction between light and geometry by using a rendered shading reference to spatially modulate the DDIM. We achieve state-of-the-art performance on standard benchmark Multi-PIE and can photorealistically relight in-the-wild images. Please visit our page: https://diffusion-face-relighting.github.io

One of the most common things that a genealogist is tasked with is the gathering of a person's initial family history, normally via in-person interviews or with the use of a platform such as ancestry.com, as this can provide a strong foundation upon which a genealogist may build. However, the ability to conduct these interviews can often be hindered by both geographical constraints and the technical proficiency of the interviewee, as the interviewee in these types of interviews is most often an elderly person with a lower than average level of technical proficiency. With this in mind, this study presents what we believe, based on prior research, to be the first chatbot geared entirely towards the gathering of family histories, and explores the viability of utilising such a chatbot by comparing the performance and usability of such a method with the aforementioned alternatives. With a chatbot-based approach, we show that, though the average time taken to conduct an interview may be longer than if the user had used ancestry.com or participated in an in-person interview, the number of mistakes made and the level of confusion from the user regarding the UI and process required is lower than the other two methods. Note that the final metric regarding the user's confusion is not applicable for the in-person interview sessions due to its lack of a UI. With refinement, we believe this use of a chatbot could be a valuable tool for genealogists, especially when dealing with interviewees who are based in other countries where it is not possible to conduct an in-person interview.

In recent years, there is a growing interest in combining techniques attributed to the areas of Statistics and Machine Learning in order to obtain the benefits of both approaches. In this article, the statistical technique lasso for variable selection is represented through a neural network. It is observed that, although both the statistical approach and its neural version have the same objective function, they differ due to their optimization. In particular, the neural version is usually optimized in one-step using a single validation set, while the statistical counterpart uses a two-step optimization based on cross-validation. The more elaborated optimization of the statistical method results in more accurate parameter estimation, especially when the training set is small. For this reason, a modification of the standard approach for training neural networks, that mimics the statistical framework, is proposed. During the development of the above modification, a new optimization algorithm for identifying the significant variables emerged. Experimental results, using synthetic and real data sets, show that this new optimization algorithm achieves better performance than any of the three previous optimization approaches.

The utility of reinforcement learning is limited by the alignment of reward functions with the interests of human stakeholders. One promising method for alignment is to learn the reward function from human-generated preferences between pairs of trajectory segments, a type of reinforcement learning from human feedback (RLHF). These human preferences are typically assumed to be informed solely by partial return, the sum of rewards along each segment. We find this assumption to be flawed and propose modeling human preferences instead as informed by each segment's regret, a measure of a segment's deviation from optimal decision-making. Given infinitely many preferences generated according to regret, we prove that we can identify a reward function equivalent to the reward function that generated those preferences, and we prove that the previous partial return model lacks this identifiability property in multiple contexts. We empirically show that our proposed regret preference model outperforms the partial return preference model with finite training data in otherwise the same setting. Additionally, we find that our proposed regret preference model better predicts real human preferences and also learns reward functions from these preferences that lead to policies that are better human-aligned. Overall, this work establishes that the choice of preference model is impactful, and our proposed regret preference model provides an improvement upon a core assumption of recent research. We have open sourced our experimental code, the human preferences dataset we gathered, and our training and preference elicitation interfaces for gathering a such a dataset.

Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only capture the knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet -- where billions of images are uploaded each day. We suggest an alternate approach: rather than hoping our static datasets transfer to our desired tasks after large-scale pre-training, we propose dynamically utilizing the Internet to quickly train a small-scale model that does extremely well on the task at hand. Our approach, called Internet Explorer, explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next. We evaluate Internet Explorer across several datasets and show that it outperforms or matches CLIP oracle performance by using just a single GPU desktop to actively query the Internet for 30--40 hours. Results, visualizations, and videos at https://internet-explorer-ssl.github.io/

This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions -- temporal stationarity and individual homogeneity are both violated. To handle the ``double inhomogeneities", we propose a class of latent factor models for the reward and observation transition functions, under which we develop a general OPE framework that consists of both model-based and model-free approaches. To our knowledge, this is the first paper that develops statistically sound OPE methods in offline RL with double inhomogeneities. It contributes to a deeper understanding of OPE in environments, where standard RL assumptions are not met, and provides several practical approaches in these settings. We establish the theoretical properties of the proposed value estimators and empirically show that our approach outperforms competing methods that ignore either temporal nonstationarity or individual heterogeneity. Finally, we illustrate our method on a data set from the Medical Information Mart for Intensive Care.

This work seeks to answer key research questions regarding the viability of reinforcement learning over the S&P 500 index. The on-policy techniques of Value Iteration (VI) and State-action-reward-state-action (SARSA) are implemented along with the off-policy technique of Q-Learning. The models are trained and tested on a dataset comprising multiple years of stock market data from 2000-2023. The analysis presents the results and findings from training and testing the models using two different time periods: one including the COVID-19 pandemic years and one excluding them. The results indicate that including market data from the COVID-19 period in the training dataset leads to superior performance compared to the baseline strategies. During testing, the on-policy approaches (VI and SARSA) outperform Q-learning, highlighting the influence of bias-variance tradeoff and the generalization capabilities of simpler policies. However, it is noted that the performance of Q-learning may vary depending on the stability of future market conditions. Future work is suggested, including experiments with updated Q-learning policies during testing and trading diverse individual stocks. Additionally, the exploration of alternative economic indicators for training the models is proposed.

Adversarial attacks dramatically change the output of an otherwise accurate learning system using a seemingly inconsequential modification to a piece of input data. Paradoxically, empirical evidence indicates that even systems which are robust to large random perturbations of the input data remain susceptible to small, easily constructed, adversarial perturbations of their inputs. Here, we show that this may be seen as a fundamental feature of classifiers working with high dimensional input data. We introduce a simple generic and generalisable framework for which key behaviours observed in practical systems arise with high probability -- notably the simultaneous susceptibility of the (otherwise accurate) model to easily constructed adversarial attacks, and robustness to random perturbations of the input data. We confirm that the same phenomena are directly observed in practical neural networks trained on standard image classification problems, where even large additive random noise fails to trigger the adversarial instability of the network. A surprising takeaway is that even small margins separating a classifier's decision surface from training and testing data can hide adversarial susceptibility from being detected using randomly sampled perturbations. Counterintuitively, using additive noise during training or testing is therefore inefficient for eradicating or detecting adversarial examples, and more demanding adversarial training is required.

Time series classification is an important data mining task that has received a lot of interest in the past two decades. Due to the label scarcity in practice, semi-supervised time series classification with only a few labeled samples has become popular. Recently, Similarity-aware Time Series Classification (SimTSC) is proposed to address this problem by using a graph neural network classification model on the graph generated from pairwise Dynamic Time Warping (DTW) distance of batch data. It shows excellent accuracy and outperforms state-of-the-art deep learning models in several few-label settings. However, since SimTSC relies on pairwise DTW distances, the quadratic complexity of DTW limits its usability to only reasonably sized datasets. To address this challenge, we propose a new efficient semi-supervised time series classification technique, LB-SimTSC, with a new graph construction module. Instead of using DTW, we propose to utilize a lower bound of DTW, LB_Keogh, to approximate the dissimilarity between instances in linear time, while retaining the relative proximity relationships one would have obtained via computing DTW. We construct the pairwise distance matrix using LB_Keogh and build a graph for the graph neural network. We apply this approach to the ten largest datasets from the well-known UCR time series classification archive. The results demonstrate that this approach can be up to 104x faster than SimTSC when constructing the graph on large datasets without significantly decreasing classification accuracy.  ( 3 min )

Drug repositioning-a promising strategy for discovering new therapeutic uses for existing drugs-has been increasingly explored in the computational science literature using biomedical databases. However, the technological potential of drug repositioning candidates has often been overlooked. This study presents a novel protocol to comprehensively analyse various sources such as pharmaceutical patents and biomedical databases, and identify drug repositioning candidates with both technological potential and scientific evidence. To this end, first, we constructed a scientific biomedical knowledge graph (s-BKG) comprising relationships between drugs, diseases, and genes derived from biomedical databases. Our protocol involves identifying drugs that exhibit limited association with the target disease but are closely located in the s-BKG, as potential drug candidates. We constructed a patent-informed biomedical knowledge graph (p-BKG) by adding pharmaceutical patent information. Finally, we developed a graph embedding protocol to ascertain the structure of the p-BKG, thereby calculating the relevance scores of those candidates with target disease-related patents to evaluate their technological potential. Our case study on Alzheimer's disease demonstrates its efficacy and feasibility, while the quantitative outcomes and systematic methods are expected to bridge the gap between computational discoveries and successful market applications in drug repositioning research.  ( 2 min )

Motivation: We explored how explainable AI (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent representations inside of transformer models, which were finetuned to Gene Ontology term and Enzyme Commission number prediction, can be inspected too. Results: The approach enabled us to identify amino acids in the sequences that the transformers pay particular attention to, and to show that these relevant sequence parts reflect expectations from biology and chemistry, both in the embedding layer and inside of the model, where we identified transformer heads with a statistically significant correspondence of attribution maps with ground truth sequence annotations (e.g., transmembrane regions, active sites) across many proteins. Availability and Implementation: Source code can be accessed at https://github.com/markuswenzel/xai-proteins .  ( 2 min )

We study the problem of estimating mixtures of Gaussians under the constraint of differential privacy (DP). Our main result is that $\tilde{O}(k^2 d^4 \log(1/\delta) / \alpha^2 \varepsilon)$ samples are sufficient to estimate a mixture of $k$ Gaussians up to total variation distance $\alpha$ while satisfying $(\varepsilon, \delta)$-DP. This is the first finite sample complexity upper bound for the problem that does not make any structural assumptions on the GMMs. To solve the problem, we devise a new framework which may be useful for other tasks. On a high level, we show that if a class of distributions (such as Gaussians) is (1) list decodable and (2) admits a "locally small'' cover [BKSW19] with respect to total variation distance, then the class of its mixtures is privately learnable. The proof circumvents a known barrier indicating that, unlike Gaussians, GMMs do not admit a locally small cover [AAL21].  ( 2 min )

Rising urban populations have led to a surge in vehicle use and made traffic monitoring and management indispensable. Acoustic traffic monitoring (ATM) offers a cost-effective and efficient alternative to more computationally expensive methods of monitoring traffic such as those involving computer vision technologies. In this paper, we present MVD and MVDA: two open datasets for the development of acoustic traffic monitoring and vehicle-type classification algorithms, which contain audio recordings of moving vehicles. The dataset contain four classes- Trucks, Cars, Motorbikes, and a No-vehicle class. Additionally, we propose a novel and efficient way to accurately classify these acoustic signals using cepstrum and spectrum based local and global audio features, and a multi-input neural network. Experimental results show that our methodology improves upon the established baselines of previous works and achieves an accuracy of 91.98% and 96.66% on MVD and MVDA Datasets, respectively. Finally, the proposed model was deployed through an Android application to make it accessible for testing and demonstrate its efficacy.  ( 2 min )

This paper presents a novel reconstruction method that leverages Diffusion Models to protect machine learning classifiers against adversarial attacks, all without requiring any modifications to the classifiers themselves. The susceptibility of machine learning models to minor input perturbations renders them vulnerable to adversarial attacks. While diffusion-based methods are typically disregarded for adversarial defense due to their slow reverse process, this paper demonstrates that our proposed method offers robustness against adversarial threats while preserving clean accuracy, speed, and plug-and-play compatibility. Code at: https://github.com/HondamunigePrasannaSilva/DiffDefence.  ( 2 min )

The representation space of neural models for textual data emerges in an unsupervised manner during training. Understanding how those representations encode human-interpretable concepts is a fundamental problem. One prominent approach for the identification of concepts in neural representations is searching for a linear subspace whose erasure prevents the prediction of the concept from the representations. However, while many linear erasure algorithms are tractable and interpretable, neural networks do not necessarily represent concepts in a linear manner. To identify non-linearly encoded concepts, we propose a kernelization of a linear minimax game for concept erasure. We demonstrate that it is possible to prevent specific non-linear adversaries from predicting the concept. However, the protection does not transfer to different nonlinear adversaries. Therefore, exhaustively erasing a non-linearly encoded concept remains an open problem.  ( 2 min )

Understanding customer behavior in retail stores plays a crucial role in improving customer satisfaction by adding personalized value to services. Behavior analysis reveals both general and detailed patterns in the interaction of customers with a store items and other people, providing store managers with insight into customer preferences. Several solutions aim to utilize this data by recognizing specific behaviors through statistical visualization. However, current approaches are limited to the analysis of small customer behavior sets, utilizing conventional methods to detect behaviors. They do not use deep learning techniques such as deep neural networks, which are powerful methods in the field of computer vision. Furthermore, these methods provide limited figures when visualizing the behavioral data acquired by the system. In this study, we propose a framework that includes three primary parts: mathematical modeling of customer behaviors, behavior analysis using an efficient deep learning based system, and individual and group behavior visualization. Each module and the entire system were validated using data from actual situations in a retail store.  ( 2 min )

Personalized federated learning has received an upsurge of attention due to the mediocre performance of conventional federated learning (FL) over heterogeneous data. Unlike conventional FL which trains a single global consensus model, personalized FL allows different models for different clients. However, existing personalized FL algorithms only implicitly transfer the collaborative knowledge across the federation by embedding the knowledge into the aggregated model or regularization. We observed that this implicit knowledge transfer fails to maximize the potential of each client's empirical risk toward other clients. Based on our observation, in this work, we propose Personalized Global Federated Learning (PGFed), a novel personalized FL framework that enables each client to personalize its own global objective by explicitly and adaptively aggregating the empirical risks of itself and other clients. To avoid massive (O(N^2)) communication overhead and potential privacy leakage while achieving this, each client's risk is estimated through a first-order approximation for other clients' adaptive risk aggregation. On top of PGFed, we develop a momentum upgrade, dubbed PGFedMo, to more efficiently utilize clients' empirical risks. Our extensive experiments on four datasets under different federated settings show consistent improvements of PGFed over previous state-of-the-art methods. The code is publicly available at https://github.com/ljaiverson/pgfed.  ( 2 min )

Multiscale stochastic dynamical systems have been widely adopted to scientific and engineering problems due to their capability of depicting complex phenomena in many real world applications. This work is devoted to investigating the effective reduced dynamics for a slow-fast stochastic dynamical system. Given observation data on a short-term period satisfying some unknown slow-fast stochastic system, we propose a novel algorithm including a neural network called Auto-SDE to learn invariant slow manifold. Our approach captures the evolutionary nature of a series of time-dependent autoencoder neural networks with the loss constructed from a discretized stochastic differential equation. Our algorithm is also proved to be accurate, stable and effective through numerical experiments under various evaluation metrics.  ( 2 min )

Labeling neural network submodules with human-legible descriptions is useful for many downstream tasks: such descriptions can surface failures, guide interventions, and perhaps even explain important model behaviors. To date, most mechanistic descriptions of trained networks have involved small models, narrowly delimited phenomena, and large amounts of human labor. Labeling all human-interpretable sub-computations in models of increasing size and complexity will almost certainly require tools that can generate and validate descriptions automatically. Recently, techniques that use learned models in-the-loop for labeling have begun to gain traction, but methods for evaluating their efficacy are limited and ad-hoc. How should we validate and compare open-ended labeling tools? This paper introduces FIND (Function INterpretation and Description), a benchmark suite for evaluating the building blocks of automated interpretability methods. FIND contains functions that resemble components of trained neural networks, and accompanying descriptions of the kind we seek to generate. The functions are procedurally constructed across textual and numeric domains, and involve a range of real-world complexities, including noise, composition, approximation, and bias. We evaluate new and existing methods that use language models (LMs) to produce code-based and language descriptions of function behavior. We find that an off-the-shelf LM augmented with only black-box access to functions can sometimes infer their structure, acting as a scientist by forming hypotheses, proposing experiments, and updating descriptions in light of new data. However, LM-based descriptions tend to capture global function behavior and miss local corruptions. These results show that FIND will be useful for characterizing the performance of more sophisticated interpretability methods before they are applied to real-world models.  ( 3 min )

Early warnings for dynamical transitions in complex systems or high-dimensional observation data are essential in many real world applications, such as gene mutation, brain diseases, natural disasters, financial crises, and engineering reliability. To effectively extract early warning signals, we develop a novel approach: the directed anisotropic diffusion map that captures the latent evolutionary dynamics in low-dimensional manifold. Applying the methodology to authentic electroencephalogram (EEG) data, we successfully find the appropriate effective coordinates, and derive early warning signals capable of detecting the tipping point during the state transition. Our method bridges the latent dynamics with the original dataset. The framework is validated to be accurate and effective through numerical experiments, in terms of density and transition probability. It is shown that the second coordinate holds meaningful information for critical transition in various evaluation metrics.  ( 2 min )

Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LLMs remain confined behind proprietary walls, hindering scientific progress. Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context. To address this, we have trained XGen, a series of 7B parameter models on up to 8K sequence length for up to 1.5T tokens. We have also finetuned the XGen models on public-domain instructional data, creating their instruction-tuned counterparts (XGen-Inst). We open-source our models for both research advancements and commercial applications. Our evaluation on standard benchmarks shows that XGen models achieve comparable or better results when compared with state-of-the-art open-source LLMs. Our targeted evaluation on long sequence modeling tasks shows the benefits of our 8K-sequence models over 2K-sequence open-source LLMs.  ( 2 min )

High Dynamic Range (HDR) content creation has become an important topic for modern media and entertainment sectors, gaming and Augmented/Virtual Reality industries. Many methods have been proposed to recreate the HDR counterparts of input Low Dynamic Range (LDR) images/videos given a single exposure or multi-exposure LDRs. The state-of-the-art methods focus primarily on the preservation of the reconstruction's structural similarity and the pixel-wise accuracy. However, these conventional approaches do not emphasize preserving the artistic intent of the images in terms of human visual perception, which is an essential element in media, entertainment and gaming. In this paper, we attempt to study and fill this gap. We propose an architecture called ArtHDR-Net based on a Convolutional Neural Network that uses multi-exposed LDR features as input. Experimental results show that ArtHDR-Net can achieve state-of-the-art performance in terms of the HDR-VDP-2 score (i.e., mean opinion score index) while reaching competitive performance in terms of PSNR and SSIM.  ( 2 min )

In this work, we proposed a novel and general method to construct tight frames on graphs with compact supports based on a series of hierarchical partitions. Starting from our abstract construction that generalizes previous methods based on partition trees, we are able to flexibly incorporate subgraph Laplacians into our design of graph frames. Consequently, our general methods permit adjusting the (subgraph) vanishing moments of the framelets and extra properties, such as directionality, for efficiently representing graph signals with path-like supports. Several variants are explicitly defined and tested. Experimental results show our proposed graph frames perform superiorly in non-linear approximation tasks.  ( 2 min )

Geospatial sciences include a wide range of applications, from environmental monitoring transportation to infrastructure planning, as well as location-based analysis and services. Graph theory algorithms in mathematics have emerged as indispensable tools in these domains due to their capability to model and analyse spatial relationships efficiently. This technical report explores the applications of graph theory algorithms in geospatial sciences, highlighting their role in network analysis, spatial connectivity, geographic information systems, and various other spatial problem-solving scenarios. It provides a comprehensive idea about the key concepts and algorithms of graph theory that assist the modelling processes. The report provides insights into the practical significance of graph theory in addressing real-world geospatial challenges and opportunities. It lists the extensive research, innovative technologies and methodologies implemented in this field.  ( 2 min )

Multi-task learning (MTL) is a powerful approach in deep learning that leverages the information from multiple tasks during training to improve model performance. In medical imaging, MTL has shown great potential to solve various tasks. However, existing MTL architectures in medical imaging are limited in sharing information across tasks, reducing the potential performance improvements of MTL. In this study, we introduce a novel attention-based MTL framework to better leverage inter-task interactions for various tasks from pixel-level to image-level predictions. Specifically, we propose a Cross-Task Attention Network (CTAN) which utilizes cross-task attention mechanisms to incorporate information by interacting across tasks. We validated CTAN on four medical imaging datasets that span different domains and tasks including: radiation treatment planning prediction using planning CT images of two different target cancers (Prostate, OpenKBP); pigmented skin lesion segmentation and diagnosis using dermatoscopic images (HAM10000); and COVID-19 diagnosis and severity prediction using chest CT scans (STOIC). Our study demonstrates the effectiveness of CTAN in improving the accuracy of medical imaging tasks. Compared to standard single-task learning (STL), CTAN demonstrated a 4.67% improvement in performance and outperformed both widely used MTL baselines: hard parameter sharing (HPS) with an average performance improvement of 3.22%; and multi-task attention network (MTAN) with a relative decrease of 5.38%. These findings highlight the significance of our proposed MTL framework in solving medical imaging tasks and its potential to improve their accuracy across domains.  ( 3 min )

Knowledge graph (KG) embedding has been used to benefit the diagnosis of animal diseases by analyzing electronic medical records (EMRs), such as notes and veterinary records. However, learning representations to capture entities and relations with literal information in KGs is challenging as the KGs show heterogeneous properties and various types of literal information. Meanwhile, the existing methods mostly aim to preserve graph structures surrounding target nodes without considering different types of literals, which could also carry significant information. In this paper, we propose a knowledge graph embedding model for the efficient diagnosis of animal diseases, which could learn various types of literal information and graph structure and fuse them into unified representations, namely LiteralKG. Specifically, we construct a knowledge graph that is built from EMRs along with literal information collected from various animal hospitals. We then fuse different types of entities and node feature information into unified vector representations through gate networks. Finally, we propose a self-supervised learning task to learn graph structure in pretext tasks and then towards various downstream tasks. Experimental results on link prediction tasks demonstrate that our model outperforms the baselines that consist of state-of-the-art models. The source code is available at https://github.com/NSLab-CUK/LiteralKG.  ( 2 min )

The Cancer Registry of Norway (CRN) collects information on cancer patients by receiving cancer messages from different medical entities (e.g., medical labs, and hospitals) in Norway. Such messages are validated by an automated cancer registry system: GURI. Its correct operation is crucial since it lays the foundation for cancer research and provides critical cancer-related statistics to its stakeholders. Constructing a cyber-cyber digital twin (CCDT) for GURI can facilitate various experiments and advanced analyses of the operational state of GURI without requiring intensive interactions with the real system. However, GURI constantly evolves due to novel medical diagnostics and treatment, technological advances, etc. Accordingly, CCDT should evolve as well to synchronize with GURI. A key challenge of achieving such synchronization is that evolving CCDT needs abundant data labelled by the new GURI. To tackle this challenge, we propose EvoCLINICAL, which considers the CCDT developed for the previous version of GURI as the pretrained model and fine-tunes it with the dataset labelled by querying a new GURI version. EvoCLINICAL employs a genetic algorithm to select an optimal subset of cancer messages from a candidate dataset and query GURI with it. We evaluate EvoCLINICAL on three evolution processes. The precision, recall, and F1 score are all greater than 91%, demonstrating the effectiveness of EvoCLINICAL. Furthermore, we replace the active learning part of EvoCLINICAL with random selection to study the contribution of transfer learning to the overall performance of EvoCLINICAL. Results show that employing active learning in EvoCLINICAL increases its performances consistently.  ( 3 min )

Deep metric learning (DML) aims to minimize empirical expected loss of the pairwise intra-/inter- class proximity violations in the embedding space. We relate DML to feasibility problem of finite chance constraints. We show that minimizer of proxy-based DML satisfies certain chance constraints, and that the worst case generalization performance of the proxy-based methods can be characterized by the radius of the smallest ball around a class proxy to cover the entire domain of the corresponding class samples, suggesting multiple proxies per class helps performance. To provide a scalable algorithm as well as exploiting more proxies, we consider the chance constraints implied by the minimizers of proxy-based DML instances and reformulate DML as finding a feasible point in intersection of such constraints, resulting in a problem to be approximately solved by iterative projections. Simply put, we repeatedly train a regularized proxy-based loss and re-initialize the proxies with the embeddings of the deliberately selected new samples. We applied our method with 4 well-accepted DML losses and show the effectiveness with extensive evaluations on 4 popular DML benchmarks. Code is available at: https://github.com/yetigurbuz/ccp-dml

In recent years, there is a growing interest in combining techniques attributed to the areas of Statistics and Machine Learning in order to obtain the benefits of both approaches. In this article, the statistical technique lasso for variable selection is represented through a neural network. It is observed that, although both the statistical approach and its neural version have the same objective function, they differ due to their optimization. In particular, the neural version is usually optimized in one-step using a single validation set, while the statistical counterpart uses a two-step optimization based on cross-validation. The more elaborated optimization of the statistical method results in more accurate parameter estimation, especially when the training set is small. For this reason, a modification of the standard approach for training neural networks, that mimics the statistical framework, is proposed. During the development of the above modification, a new optimization algorithm for identifying the significant variables emerged. Experimental results, using synthetic and real data sets, show that this new optimization algorithm achieves better performance than any of the three previous optimization approaches.

The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4. However, many commercial conversational agent development tools are pipeline-based and have limitations in holding a human-like conversation. This paper investigates the capabilities of LLMs to enhance pipeline-based conversational agents during two phases: 1) in the design and development phase and 2) during operations. In 1) LLMs can aid in generating training data, extracting entities and synonyms, localization, and persona design. In 2) LLMs can assist in contextualization, intent classification to prevent conversational breakdown and handle out-of-scope questions, auto-correcting utterances, rephrasing responses, formulating disambiguation questions, summarization, and enabling closed question-answering capabilities. We conducted informal experiments with GPT-4 in the private banking domain to demonstrate the scenarios above with a practical example. Companies may be hesitant to replace their pipeline-based agents with LLMs entirely due to privacy concerns and the need for deep integration within their existing ecosystems. A hybrid approach in which LLMs' are integrated into the pipeline-based agents allows them to save time and costs of building and running agents by capitalizing on the capabilities of LLMs while retaining the integration and privacy safeguards of their existing systems.

Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps. We propose a faster and cheaper approach that adds noise not until the data become pure random noise, but until they reach a hidden noisy data distribution that we can confidently learn. Then, we use fewer reverse steps to generate data by starting from this hidden distribution that is made similar to the noisy data. We reveal that the proposed model can be cast as an adversarial auto-encoder empowered by both the diffusion process and a learnable implicit prior. Experimental results show even with a significantly smaller number of reverse diffusion steps, the proposed truncated diffusion probabilistic models can provide consistent improvements over the non-truncated ones in terms of performance in both unconditional and text-guided image generations.

As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In practice, new input data tend to come without target labels. Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions. We suggest a novel approach that models how explanation characteristics shift when affected by distribution shifts. We find that the modeling of explanation shifts can be a better indicator for detecting out-of-distribution model behaviour than state-of-the-art techniques. We analyze different types of distribution shifts using synthetic examples and real-world data sets. We provide an algorithmic method that allows us to inspect the interaction between data set features and learned models and compare them to the state-of-the-art. We release our methods in an open-source Python package, as well as the code used to reproduce our experiments.

Consider two forecasters, each making a single prediction for a sequence of events over time. We ask a relatively basic question: how might we compare these forecasters, either online or post-hoc, while avoiding unverifiable assumptions on how the forecasts and outcomes were generated? In this paper, we present a rigorous answer to this question by designing novel sequential inference procedures for estimating the time-varying difference in forecast scores. To do this, we employ confidence sequences (CS), which are sequences of confidence intervals that can be continuously monitored and are valid at arbitrary data-dependent stopping times ("anytime-valid"). The widths of our CSs are adaptive to the underlying variance of the score differences. Underlying their construction is a game-theoretic statistical framework, in which we further identify e-processes and p-processes for sequentially testing a weak null hypothesis -- whether one forecaster outperforms another on average (rather than always). Our methods do not make distributional assumptions on the forecasts or outcomes; our main theorems apply to any bounded scores, and we later provide alternative methods for unbounded scores. We empirically validate our approaches by comparing real-world baseball and weather forecasters.

Given pairwise comparisons between multiple items, how to rank them so that the ranking matches the observations? This problem, known as rank aggregation, has found many applications in sports, recommendation systems, and other web applications. As it is generally NP-hard to find a global ranking that minimizes the mismatch (known as the Kemeny optimization), we focus on the Erd\"os-R\'enyi outliers (ERO) model for this ranking problem. Here, each pairwise comparison is a corrupted copy of the true score difference. We investigate spectral ranking algorithms that are based on unnormalized and normalized data matrices. The key is to understand their performance in recovering the underlying scores of each item from the observed data. This reduces to deriving an entry-wise perturbation error bound between the top eigenvectors of the unnormalized/normalized data matrix and its population counterpart. By using the leave-one-out technique, we provide a sharper $\ell_{\infty}$-norm perturbation bound of the eigenvectors and also derive an error bound on the maximum displacement for each item, with only $\Omega(n\log n)$ samples. Our theoretical analysis improves upon the state-of-the-art results in terms of sample complexity, and our numerical experiments confirm these theoretical findings.

Modern applications of survival analysis increasingly involve time-dependent covariates. The Python package BoXHED2.0 is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from www.github.com/BoXHED.

Interpolators are unstable. For example, the mininum $\ell_2$ norm least square interpolator exhibits unbounded test errors when dealing with noisy data. In this paper, we study how ensemble stabilizes and thus improves the generalization performance, measured by the out-of-sample prediction risk, of an individual interpolator. We focus on bagged linear interpolators, as bagging is a popular randomization-based ensemble method that can be implemented in parallel. We introduce the multiplier-bootstrap-based bagged least square estimator, which can then be formulated as an average of the sketched least square estimators. The proposed multiplier bootstrap encompasses the classical bootstrap with replacement as a special case, along with a more intriguing variant which we call the Bernoulli bootstrap. Focusing on the proportional regime where the sample size scales proportionally with the feature dimensionality, we investigate the out-of-sample prediction risks of the sketched and bagged least square estimators in both underparametrized and overparameterized regimes. Our results reveal the statistical roles of sketching and bagging. In particular, sketching modifies the aspect ratio and shifts the interpolation threshold of the minimum $\ell_2$ norm estimator. However, the risk of the sketched estimator continues to be unbounded around the interpolation threshold due to excessive variance. In stark contrast, bagging effectively mitigates this variance, leading to a bounded limiting out-of-sample prediction risk. To further understand this stability improvement property, we establish that bagging acts as a form of implicit regularization, substantiated by the equivalence of the bagged estimator with its explicitly regularized counterpart. We also discuss several extensions.

There has been much recent progress in forecasting the next observation of a linear dynamical system (LDS), which is known as the improper learning, as well as in the estimation of its system matrices, which is known as the proper learning of LDS. We present an approach to proper learning of LDS, which in spite of the non-convexity of the problem, guarantees global convergence of numerical solutions to a least-squares estimator. We present promising computational results.

Bootstrapping is behind much of the successes of deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values. Target Networks are employed to stabilize training by using an additional set of lagging parameters to estimate the target values. Despite the popularity of Target Networks, their effect on the optimization is still misunderstood. In this work, we show that they act as an implicit regularizer which can be beneficial in some cases, but also have disadvantages such as being inflexible and can result in instabilities, even when vanilla TD(0) converges. To overcome these issues, we propose an explicit Functional Regularization alternative that is flexible and a convex regularizer in function space and we theoretically study its convergence. We conduct an experimental study across a range of environments, discount factors, and off-policiness data collections to investigate the effectiveness of the regularization induced by Target Networks and Functional Regularization in terms of performance, accuracy, and stability. Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement. Furthermore, adjusting both the regularization weight and the network update period in Functional Regularization can result in further performance improvements compared to solely adjusting the network update period as typically done with Target Networks. Our approach also enhances the ability to networks to recover accurate $Q$-values.

Triplet Markov chains are general generative models for sequential data which take into account three kinds of random variables: (noisy) observations, their associated discrete labels and latent variables which aim at strengthening the distribution of the observations and their associated labels. However, in practice, we do not have at our disposal all the labels associated to the observations to estimate the parameters of such models. In this paper, we propose a general framework based on a variational Bayesian inference to train parameterized triplet Markov chain models in a semi-supervised context. The generality of our approach enables us to derive semi-supervised algorithms for a variety of generative models for sequential Bayesian classification.

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, has been recently extended to exploit prior knowledge on the cardinality of each cluster. Such knowledge is used to increase performance as well as solution quality. In this paper, we propose a global optimization approach based on the branch-and-cut technique to solve the cardinality-constrained MSSC. For the lower bound routine, we use the semidefinite programming (SDP) relaxation recently proposed by Rujeerapaiboon et al. [SIAM J. Optim. 29(2), 1211-1239, (2019)]. However, this relaxation can be used in a branch-and-cut method only for small-size instances. Therefore, we derive a new SDP relaxation that scales better with the instance size and the number of clusters. In both cases, we strengthen the bound by adding polyhedral cuts. Benefiting from a tailored branching strategy which enforces pairwise constraints, we reduce the complexity of the problems arising in the children nodes. For the upper bound, instead, we present a local search procedure that exploits the solution of the SDP relaxation solved at each node. Computational results show that the proposed algorithm globally solves, for the first time, real-world instances of size 10 times larger than those solved by state-of-the-art exact methods.

This paper considers an empirical risk minimization problem under heavy-tailed settings, where data does not have finite variance, but only has $p$-th moment with $p \in (1,2)$. Instead of using estimation procedure based on truncated observed data, we choose the optimizer by minimizing the risk value. Those risk values can be robustly estimated via using the remarkable Catoni's method (Catoni, 2012). Thanks to the structure of Catoni-type influence functions, we are able to establish excess risk upper bounds via using generalized generic chaining methods. Moreover, we take computational issues into consideration. We especially theoretically investigate two types of optimization methods, robust gradient descent algorithm and empirical risk-based methods. With an extensive numerical study, we find that the optimizer based on empirical risks via Catoni-style estimation indeed shows better performance than other baselines. It indicates that estimation directly based on truncated data may lead to unsatisfactory results.

Early warnings for dynamical transitions in complex systems or high-dimensional observation data are essential in many real world applications, such as gene mutation, brain diseases, natural disasters, financial crises, and engineering reliability. To effectively extract early warning signals, we develop a novel approach: the directed anisotropic diffusion map that captures the latent evolutionary dynamics in low-dimensional manifold. Applying the methodology to authentic electroencephalogram (EEG) data, we successfully find the appropriate effective coordinates, and derive early warning signals capable of detecting the tipping point during the state transition. Our method bridges the latent dynamics with the original dataset. The framework is validated to be accurate and effective through numerical experiments, in terms of density and transition probability. It is shown that the second coordinate holds meaningful information for critical transition in various evaluation metrics.

Multiscale stochastic dynamical systems have been widely adopted to scientific and engineering problems due to their capability of depicting complex phenomena in many real world applications. This work is devoted to investigating the effective reduced dynamics for a slow-fast stochastic dynamical system. Given observation data on a short-term period satisfying some unknown slow-fast stochastic system, we propose a novel algorithm including a neural network called Auto-SDE to learn invariant slow manifold. Our approach captures the evolutionary nature of a series of time-dependent autoencoder neural networks with the loss constructed from a discretized stochastic differential equation. Our algorithm is also proved to be accurate, stable and effective through numerical experiments under various evaluation metrics.

This paper explores the expressive power of deep neural networks for a diverse range of activation functions. An activation function set $\mathscr{A}$ is defined to encompass the majority of commonly used activation functions, such as $\mathtt{ReLU}$, $\mathtt{LeakyReLU}$, $\mathtt{ReLU}^2$, $\mathtt{ELU}$, $\mathtt{SELU}$, $\mathtt{Softplus}$, $\mathtt{GELU}$, $\mathtt{SiLU}$, $\mathtt{Swish}$, $\mathtt{Mish}$, $\mathtt{Sigmoid}$, $\mathtt{Tanh}$, $\mathtt{Arctan}$, $\mathtt{Softsign}$, $\mathtt{dSiLU}$, and $\mathtt{SRS}$. We demonstrate that for any activation function $\varrho\in \mathscr{A}$, a $\mathtt{ReLU}$ network of width $N$ and depth $L$ can be approximated to arbitrary precision by a $\varrho$-activated network of width $4N$ and depth $2L$ on any bounded set. This finding enables the extension of most approximation results achieved with $\mathtt{ReLU}$ networks to a wide variety of other activation functions, at the cost of slightly larger constants.

We study the problem of estimating mixtures of Gaussians under the constraint of differential privacy (DP). Our main result is that $\tilde{O}(k^2 d^4 \log(1/\delta) / \alpha^2 \varepsilon)$ samples are sufficient to estimate a mixture of $k$ Gaussians up to total variation distance $\alpha$ while satisfying $(\varepsilon, \delta)$-DP. This is the first finite sample complexity upper bound for the problem that does not make any structural assumptions on the GMMs. To solve the problem, we devise a new framework which may be useful for other tasks. On a high level, we show that if a class of distributions (such as Gaussians) is (1) list decodable and (2) admits a "locally small'' cover [BKSW19] with respect to total variation distance, then the class of its mixtures is privately learnable. The proof circumvents a known barrier indicating that, unlike Gaussians, GMMs do not admit a locally small cover [AAL21].

This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification. We emphasize tools we deem particularly useful for a range of problems in this domain, such as the covering technique, the Hanson-Wright Inequality and the method of self-normalized martingales. We then employ these tools to give streamlined proofs of the performance of various least-squares based estimators for identifying the parameters in autoregressive models. We conclude by sketching out how the ideas presented herein can be extended to certain nonlinear identification problems.

Recent works have demonstrated that the sample complexity of gradient-based learning of single index models, i.e. functions that depend on a 1-dimensional projection of the input data, is governed by their information exponent. However, these results are only concerned with isotropic data, while in practice the input often contains additional structure which can implicitly guide the algorithm. In this work, we investigate the effect of a spiked covariance structure and reveal several interesting phenomena. First, we show that in the anisotropic setting, the commonly used spherical gradient dynamics may fail to recover the true direction, even when the spike is perfectly aligned with the target direction. Next, we show that appropriate weight normalization that is reminiscent of batch normalization can alleviate this issue. Further, by exploiting the alignment between the (spiked) input covariance and the target, we obtain improved sample complexity compared to the isotropic case. In particular, under the spiked model with a suitably large spike, the sample complexity of gradient-based training can be made independent of the information exponent while also outperforming lower bounds for rotationally invariant kernel methods.

Typical technique in knowledge distillation (KD) is regularizing the learning of a limited capacity model (student) by pushing its responses to match a powerful model's (teacher). Albeit useful especially in the penultimate layer and beyond, its action on student's feature transform is rather implicit, limiting its practice in the intermediate layers. To explicitly embed the teacher's knowledge in feature transform, we propose a learnable KD layer for the student which improves KD with two distinct abilities: i) learning how to leverage the teacher's knowledge, enabling to discard nuisance information, and ii) feeding forward the transferred knowledge deeper. Thus, the student enjoys the teacher's knowledge during the inference besides training. Formally, we repurpose 1x1-BN-ReLU-1x1 convolution block to assign a semantic vector to each local region according to the template (supervised by the teacher) that the corresponding region of the student matches. To facilitate template learning in the intermediate layers, we propose a novel form of supervision based on the teacher's decisions. Through rigorous experimentation, we demonstrate the effectiveness of our approach on 3 popular classification benchmarks. Code is available at: https://github.com/adagorgun/letKD-framework

This paper introduces the Trinary decision tree, an algorithm designed to improve the handling of missing data in decision tree regressors and classifiers. Unlike other approaches, the Trinary decision tree does not assume that missing values contain any information about the response. Both theoretical calculations on estimator bias and numerical illustrations using real data sets are presented to compare its performance with established algorithms in different missing data scenarios (Missing Completely at Random (MCAR), and Informative Missingness (IM)). Notably, the Trinary tree outperforms its peers in MCAR settings, especially when data is only missing out-of-sample, while lacking behind in IM settings. A hybrid model, the TrinaryMIA tree, which combines the Trinary tree and the Missing In Attributes (MIA) approach, shows robust performance in all types of missingness. Despite the potential drawback of slower training speed, the Trinary tree offers a promising and more accurate method of handling missing data in decision tree algorithms.

The exclusive or (xor) function is one of the simplest examples that illustrate why nonlinear feedforward networks are superior to linear regression for machine learning applications. We review the xor representation and approximation problems and discuss their solutions in terms of probabilistic logic and associative copula functions. After briefly reviewing the specification of feedforward networks, we compare the dynamics of learned error surfaces with different activation functions such as RELU and tanh through a set of colorful three-dimensional charts. The copula representations extend xor from Boolean to real values, thereby providing a convenient way to demonstrate the concept of cross-validation on in-sample and out-sample data sets. Our approach is pedagogical and is meant to be a machine learning prolegomenon.

Accurate predictions, as with machine learning, may not suffice to provide optimal healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such as racial biases. Causal thinking is needed for data-driven decisions. Here, we give an introduction to the key elements, focusing on routinely-collected data, electronic health records (EHRs) and claims data. Using such data to assess the value of an intervention requires care: temporal dependencies and existing practices easily confound the causal effect. We present a step-by-step framework to help build valid decision making from real-life patient records by emulating a randomized trial before individualizing decisions, eg with machine learning. Our framework highlights the most important pitfalls and considerations in analysing EHRs or claims data to draw causal conclusions. We illustrate the various choices in studying the effect of albumin on sepsis mortality in the Medical Information Mart for Intensive Care database (MIMIC-IV). We study the impact of various choices at every step, from feature extraction to causal-estimator selection. In a tutorial spirit, the code and the data are openly available.

This work investigates the nuanced algorithm design choices for deep learning in the presence of computational-statistical gaps. We begin by considering offline sparse parity learning, a supervised classification problem which admits a statistical query lower bound for gradient-based training of a multilayer perceptron. This lower bound can be interpreted as a multi-resource tradeoff frontier: successful learning can only occur if one is sufficiently rich (large model), knowledgeable (large dataset), patient (many training iterations), or lucky (many random guesses). We show, theoretically and experimentally, that sparse initialization and increasing network width yield significant improvements in sample efficiency in this setting. Here, width plays the role of parallel search: it amplifies the probability of finding "lottery ticket" neurons, which learn sparse features more sample-efficiently. Finally, we show that the synthetic sparse parity task can be useful as a proxy for real problems requiring axis-aligned feature learning. We demonstrate improved sample efficiency on tabular classification benchmarks by using wide, sparsely-initialized MLP models; these networks sometimes outperform tuned random forests.

The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate clustering results. A very popular measure is the Silhouette. We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its properties, provide two fast versions for the direct optimization, and discuss the use to choose the optimal number of clusters. We combine ideas from the original Silhouette with the well-known PAM algorithm and its latest improvements FasterPAM. One of the versions guarantees equal results to the original variant and provides a run speedup of $O(k^2)$. In experiments on real data with 30000 samples and $k$=100, we observed a 10464$\times$ speedup compared to the original PAMMEDSIL algorithm. Additionally, we provide a variant to choose the optimal number of clusters directly.

We introduce the $ARMOR_D$ methods as novel approaches to enhancing the adversarial robustness of deep learning models. These methods are based on a new class of optimal-transport-regularized divergences, constructed via an infimal convolution between an information divergence and an optimal-transport (OT) cost. We use these as tools to enhance adversarial robustness by maximizing the expected loss over a neighborhood of distributions, a technique known as distributionally robust optimization. Viewed as a tool for constructing adversarial samples, our method allows samples to be both transported, according to the OT cost, and re-weighted, according to the information divergence. We demonstrate the effectiveness of our method on malware detection and image recognition applications and find that, to our knowledge, it outperforms existing methods at enhancing the robustness against adversarial attacks. $ARMOR_D$ yields the robustified accuracy of $98.29\%$ against $FGSM$ and $98.18\%$ against $PGD^{40}$ on the MNIST dataset, reducing the error rate by more than $19.7\%$ and $37.2\%$ respectively compared to prior methods. Similarly, in malware detection, a discrete (binary) data domain, $ARMOR_D$ improves the robustified accuracy under $rFGSM^{50}$ attack compared to the previous best-performing adversarial training methods by $37.0\%$ while lowering false negative and false positive rates by $51.1\%$ and $57.53\%$, respectively.

Multiagent systems aim to accomplish highly complex learning tasks through decentralised consensus seeking dynamics and their use has garnered a great deal of attention in the signal processing and computational intelligence societies. This article examines the behaviour of multiagent networked systems with nonlinear filtering/learning dynamics. To this end, a general formulation for the actions of an agent in multiagent networked systems is presented and conditions for achieving a cohesive learning behaviour is given. Importantly, application of the so derived framework in distributed and federated learning scenarios are presented.  ( 2 min )

On the verge of its IPO, Instacart has introduced major AI-powered features to its Storefront platform and the smart Caper Carts. Main upgrades: conversational search powered by OpenAI's ChatGPT and inbuilt AI models. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/olqtxvwjo3nb1.png?width=750&format=png&auto=webp&s=d8eaefbb9865c51732efc2792ec386610ecd38e6 AI advancements in Instacart's infrastructure Instacart, which holds approximately 22% of the $132 billion US online grocery-delivery market, has been leaning more towards being a tech platform. The new Instacart Storefront, entailing features driven by 150 proprietary AI models, is built on the same core infrastructure as the Instacart app. Customers can engage in open-ended searches on retailers' storefronts via the search bar. AI upgrades in Caper Carts AI-powered Caper Carts by Instacart have been upgraded. Customers can now order directly from their Caper Cart and get informed when their orders are ready. Camera and weight sensor efficiency is enhanced thanks to improved AI models, ensuring a smoother shopping journey and providing an extra layer of security against suspicious activity. (source) P.S. If you want this kind of analysis, delve into the latest updates in AI with our free newsletter, already favored by professionals from Google, Meta, and OpenAI. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Tech startups are running sexually explicit ads for apps promoting not-safe-for-work experiences on platforms like Facebook, Instagram, and TikTok. These ads feature digitally created potential 'girlfriends' with large breasts and tight clothing, and some even use popular children's TV characters to promote 'NSFW pics' apps. NBC News found 35 app developers running sexually explicit ads on Meta-owned apps, and 14 app developers running similar ads on TikTok. The marketing push is part of an AI gold rush, capitalizing on the surge of interest in AI and benefiting from a double standard that hurts real human sex workers. Researchers believe that the gender-based slant in these ads reflects social media platforms allowing sex-related ads only if the intended audience is men. Meta and TikTok have stepped up their removal of sexually explicit AI ads after NBC News contacted them, but questions remain about how the ads got through their filters in the first place. Similar ads also appear in the Apple and Google app stores, although the extent of advertising there is unknown. Source : https://www.nbcnews.com/tech/social-media/ai-girlfriend-ads-instagram-tiktok-chat-pics-chatgpt-dose-rcna97547 submitted by /u/NuseAI [link] [comments]  ( 9 min )

News provided by aibrews.com Technology Innovation Institute in Abu Dhabi has released Falcon 180B - a large language model with 180 billion parameters, trained on 3.5 trillion tokens. It's currently the largest openly available model, and rivals proprietary models like PaLM-2. Falcon 180B is 2.5 times larger than Llama 2 and was trained with 4x more compute. It is available for both research and commercial use [Details]. Meta AI released Belebele, a first-of-its-kind multilingual reading comprehension dataset spanning 122 language variants, enabling direct comparison of how well models understand different languages [Details]. Meta AI has published Code Llama’s research paper with more information on training, evaluation results and safety [Paper]. Open Interpreter, an open-source, …  ( 10 min )

Hello Everyone, i have recently started working on a project, where I need to animate an image of a face in real time to speak sentences. Essentially I am trying to build a face for my own large language model. I know of Nvidia's Audio2Face and Metahuman, but these are all in 3D and take a lot of time rendering the lip and eye animations. I need something, which works only with a bit of latency. ​ Does anyone know a service or a repo I could use to animate a 2D picture to speak text? submitted by /u/Fabianslife [link] [comments]  ( 9 min )

submitted by /u/the_anonymizer [link] [comments]  ( 9 min )

This is a complete ChatGPT beginner question but has anyone ever downloaded it and used it to help with looking for specific job roles? Mainly WFH related? Or thought about changing careers and used ChatGPT to help with that? I know there are a lot of other ways to go about this but would ChatGPT help with this at all? submitted by /u/jackbowls [link] [comments]  ( 9 min )

View Poll submitted by /u/MiladMansory [link] [comments]  ( 9 min )

Professors send emails explaining that they use AI but they reviewed the grades from AI to make sure everything is fine. But students can’t use AI and then review the results just make sure everything is fine. submitted by /u/PrettyHappyAndGay [link] [comments]  ( 9 min )

A new AI tool developed by startup Delphi allows users to create virtual clones of themselves or anyone else. Users can upload an ID and add various files, such as emails, chat transcripts, and videos, to generate an AI chatbot that mimics their personality.[1] OpenAI will host its first developer conference on November 6.[2] Meta Platforms Inc. today released FACET, a benchmark dataset designed to help researchers audit computer vision models for bias.[3] Australia to require AI-made child abuse material be removed from search results.[4] Sources: [1] https://technotrenz.com/news/a-new-ai-service-allows-for-the-creation-of-a-virtual-version-of-yourself-or-a-loved-one-that-is-capable-of-making-phone-calls-on-your-behalf-2772634.html [2] https://techcrunch.com/2023/09/06/openai-will-host-its-first-developer-conference-on-november-6/ [3] https://siliconangle.com/2023/08/31/meta-releases-facet-dataset-evaluating-ai-fairness/ [4] https://www.reuters.com/technology/australia-require-ai-made-child-abuse-material-be-removed-search-results-2023-09-08/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I was thinking today about all the AI hype we have right with somewhat a bunch of new breakthroughs each month, but things not only are getting slower updates, but the updates impacts itself are becoming lesser. If that is not enough, well we have big problems ahead, such as processors are reaching the physical limit, quantum effects disrupting the works, wafers becoming increasing more expensive, the size reduction is no longer adding the same boosts in power and new materials are just far from viable. On top of this we are going meet two other walls, the software and the energy. About the first, as we make better and more complex algorithms for computation the harder it gets to make better ones to squeeze more power and handle more complex tasks. The second, is becoming more real as bi…  ( 12 min )

I used infrastructure as code (IaC) to provision and deploy Vercel's next-openai example. IaC is useful because it applies the same rigor of application code development to infrastructure provisioning. Instead of manual point and click in a cloud console which can be unrepeatable or error-prone, you just store and change all infrastructure configurations as code in source control . This example uses Pulumi which allows you to write the IaC in Python. https://github.com/aaronkao/vercel-py-openai-chatbot submitted by /u/kao-pulumi [link] [comments]  ( 9 min )

Reposting because I intended to ask about LLMs, not AI in general, and forgot that I don't need to dumb down the terminology for this sub. Thanks to the people who pointed out that mistake. --- original post --- This is something I'm curious about. I've seen a few people declaring that they're not using open source LLMs because they're GPU-poor, because the models aren't good enough, because the uis/frontends are hard to get started with, etc., and I've been wondering how much these comments and posts reflect the opinions and needs of the community as a whole. So, here's a poll. Answer away if you feel like it. I'm sharing this on a few other subs too (for the sake of greater information gathering) so please don't vote more than once. If your reasoning is not on here, feel free to comment your thoughts. If more than one option describes you, please select the one that describes you the most. View Poll submitted by /u/Heralax_Tekran [link] [comments]  ( 9 min )

I'm looking to learn more about concurrency/parallelism, optimization, data structures and algorithms from an NLP perspective. submitted by /u/Al_Miksiki [link] [comments]  ( 9 min )

Hello Everyone, I'm currently exploring the idea of a solution tailored for ML engineers and technologists. While I have a background in recruiting, I've often found myself dissatisfied with the typical recruitment process. It seems that many recruiters don't always appreciate the importance of working with candidates or understand the impact on people's livelihoods and careers. What I'm proposing is the creation of a career representation firm specifically designed for purpose-driven technologists specializing in data, product, and hardware careers. This firm would advocate for the career interests of the most passionate ML engineers. Our representation would encompass: - Strategic Career Development: Crafting a strategic approach to help engineers secure opportunities aligned with their desired projects and professional development. - Impact Matching: Identifying and connecting engineers with projects and teams where their technical skills, career goals, and personal interests can have the greatest positive impact, ensuring that your work aligns with your values and aspirations. - Industry Leadership: Positioning you as an industry leader by marketing your expertise and securing speaking engagements at conferences and other events, enhancing your professional visibility and reputation. In return for this representation, engineers would commit to a 3% fee deducted from their salary, which would support the services provided by the firm. Would you be interested in participating in such a service? If not, would you consider recommending it to someone you know? If you are in favor of this idea, what makes you believe it would be advantageous for others even if it might not be your preference? Do you think you could personally benefit from this type of career representation? Thanks! submitted by /u/Educational_Bar_6352 [link] [comments]  ( 10 min )

Paper: https://arxiv.org/abs/2308.10379 Saw someone else post about this new prompting method on the sub here so I decided to put together a run down and prompt template. Pretty interesting to see the different methods emerge and how some attempt to simulate how code runs. My rundown -> https://www.prompthub.us/blog/how-algorithm-of-thoughts-prompting-works submitted by /u/dancleary544 [link] [comments]  ( 9 min )

Hi fellow computer scientists, so I've been researching a little about transformers and meanwhile I had to understand sinusoidal positional embedding. I have found two implementations for this, after testing both approaches I found they compute different embeddings for the same position/timestep with the same embedding dimensions... shouldn't it be equal if the position and embedding dimensions are the same? This is getting me confused, because now I don't know which implementation should I consider... Do you have any suggestions to where I can look? Thank you :) submitted by /u/Christs_Elite [link] [comments]  ( 9 min )

Hi guys, I'm completely new in this field.. I have a research in civil engineering and need to learn python, machine learning and data analysis as short as possible. Where can I achieve that?? please help me by naming the best courses or any free materials available🙏 submitted by /u/Ok-Upstairs7749 [link] [comments]  ( 9 min )

I'm one of those people who always ask questions about movies because there's something they don't get or have forgotten. Especially with more complex stories, like Game of Thrones. At the moment I'm reading Wheel of Time, a rather long fantasy series. I had the idea to build the following WebApp: There is online each chapter of the series summarized separately. So in the WebApp I could ask questions about the content. In addition, I can indicate which chapter I am reading, so that it is ensured not to spoil the user. I want to avoid to train a model. I would prefer to use one of the existing open-source models, like llama. A first, primitive idea: give the LLM all the summaries and the user's question. But this would mean to give all summaries as input every time. Not only that this approach would not be elegant, the restriction in the input size (number of words) would make this possibly even impossible. Feel free to share your ideas how i could solve this :) submitted by /u/Individual-Cause-616 [link] [comments]  ( 9 min )

I have so many .mat files in a folder which have two arrays inside each .mat file. that is, for each .mat file, i have a (224*224) array and another (136,1) array. These 224*224 arrays are my X_trains for a model and these corresponding 136*1 arrays are my y_trains (labels). i can read these files as np arrays using scipy's loadmat. My problem is, is there a way to usen tf.data .Dataset object to send these to a model or there is any other way? Also using this tf.data.Dataset can i split into train, test, val data? submitted by /u/likhith-69 [link] [comments]  ( 9 min )

Hi r/MachineLearning, I built a CLI tool to benchmark 100+ LLMs for a given question. Benchmark output allows you to compare responses, response time and cost. Try it here: https://github.com/BerriAI/litellm/blob/main/cookbook/benchmark/readme.md CLI Output: Output from CLI Tool Simply select your LLMs, enter your API keys, LLM configs and run python3 benchmark.py Happy completion()! submitted by /u/Comfortable_Dirt5590 [link] [comments]  ( 9 min )

Hey! If anyone has worked with text summarization before especially with TF-IDF and extractive summarization,kindly please dm me. Hope you have a great day! submitted by /u/Ok-Avocado-5370 [link] [comments]  ( 9 min )

In the upcoming versions of macOS and iOS, Apple is including a predictive text model which offers suggestions while you type, which they’ve said to be a "transformer model". I managed to find some details about this model, including details about its topology and tokenizer, and I was even able to peek in and see several of its top predictions while typing! Blogpost: https://jackcook.com/2023/09/08/predictive-text.html Source code: https://github.com/jackcook/predictive-spy Hopefully this can give some insight into some of the trade-offs that Apple went through to put a model on every iPhone and MacBook — it’s small, it has a pretty narrow scope, and it’s not very capable on its own. Let me know what you think! submitted by /u/jackcook [link] [comments]  ( 9 min )

Follow-up on our previous post (Vision-based reinforcement learning for Trackmania: close or at superhuman level). Many comments rightfully pointed that the map we trained on: - lacked difficult features like jumps, airbrakes, drifts, ... - had not widely been played by humans We have now trained the same AI on the game's most prestigious map: Hockolicious. We also prepared a video describing the approach with much more detail. Here is our result :) AI Beats Hockolicious, Trackmania's Most Prestigious Map Note: We are still using a convolutional neural network with a structure similar to Nature's DQN paper. I am curious whether other architectures (the ResNet-like in the IMPALA paper ?) could help. Do you have any suggestions on how the neural network's vision head should be structured for that specific task? submitted by /u/Linesight_rl [link] [comments]  ( 9 min )

Hello all. I'm new to object recognition and instance segmentation. I am trying to work on a project in which I use drone imagery to detect objects that are in close formations with each other. I do this for the purpose of counting particular objects, as well as to check if an object has moved (by making a prediction on drone imagery that is taken later). Create masks? I'm now trying to understand what methodology/models make sense. First of all, should I be looking at creating masks, or do bounding boxes suffice? My idea was that masks are better, since bounding boxes overlap with each other and can miss that an object has moved slightly, Or am I wrong and are masks just an extra hassle? Or shouldn't I be looking at bounding boxes or masks at all? MaskRCNN? Model-wise, should I be lo…  ( 10 min )

I think there's a lot of confusion around AI agents today and it's mainly because of lack of definition and using the wrong terminology. We've been talking to many companies who are claiming they're working on agents but when you look under the hood, they are really just chains. I just listened to the Latent Space pod with Harrison Chase (Founder of Langchain) and I really liked how he thinks about chains vs agents. Chains: sequence of tasks in a more rigid order, where you have more control, more predictability. Agents: handling the edge-cases, the long-tail of things that can happen. And the most important thing is that it's not an OR question but an AND one: you can use them in the same application by starting with chains -> figuring our the edge-cases -> using agents to deal with them. https://preview.redd.it/l59sc4sri0nb1.png?width=3127&format=png&auto=webp&s=1f3f8730c48687eaabf1f554deb181cf35b96036 submitted by /u/BootstrapGuy [link] [comments]  ( 9 min )

I have the opportunity to see Jensen speak in the next month at a semi private event, 250-300 people. I will probably have the opportunity to ask him a question. What would you ask him? submitted by /u/Zealousideal-Food285 [link] [comments]  ( 9 min )

Greetings, people. My colleague told me about some methods of object detection/classification on 3D models, and now I'm exploring them. But during my research I couldn't find that much information about them. I would like to ask you to provide me information, literature and examples of application for them. I remember that one of the techniques is called voxelization. But still not able to find great and intuitive example. Would be thankful for any information :) submitted by /u/thattallsoldier [link] [comments]  ( 9 min )

Looking at all the popular yolo repos, v5, v7,v8, yolo-nas, all of them seem to have restrictive licenses (gpl3, agpl, apache 2) where the trained model files also falls under the license. What do people usually use to deploy detection/segmentation in production, especially with resource constraints (can't use something like fast-rcnn) submitted by /u/Appropriate_Bear_894 [link] [comments]  ( 9 min )

Hey guys! I need some help here.. many days trying to get good results but without success. So I already have the voice I want to use (edited with uvr5 and it sounds really great, without any echo or noise on the background), I trained it on aivc so that I can generate this voice verbalizing the text content I need. I used high quality - it took like 40min to generate each phrase - and it is ok, but still a little robotic. So I installed RVC and trained a model with the original voice (edited with the uvr5) just like I did the training on aivc. So I loaded the trained model on the inference tab and I selected the audio to be processed - the generated audio files from aivc. Even selecting the harvest mode, the output was worse than the generated files from aivc. I even tried to record my own voice speaking the text but it does not sound good. My trained model on rvc has 500 epochs, and it may be a very good model to use, yet idk what I’m doing wrong. Maybe I’m misusing rvc, so what I need is to improve the realism of my aivc(or tortoise) generated voices, simple as that, is rvc the best option to do this? If yes, how? Any help please would be much appreciated thanks! submitted by /u/JustSayin_thatuknow [link] [comments]  ( 9 min )

submitted by /u/hzj5790 [link] [comments]  ( 9 min )

Would you kindly share noteworthy papers that have caught your interest concerning the weights of physics-informed neural networks (PINNs)? I am looking for any innovative paper that has something to do with weights of the physics-informed neural networks or deep neural networks in general and its manipulation. Specifically, I am seeking innovative papers on weight manipulation in physics-informed neural networks. For instance papers like: Weight initialization algorithm for physics-informed neural networks using finite differences Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows Note that I am referring to the actual weights of the neural network and not the weights of the loss terms. I have to add that ideas from transfer learning are welcome too. submitted by /u/ai_physics2023 [link] [comments]  ( 9 min )

Hi ML community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs. To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into: - Performance - Time to train an LLM - Cost to train an LLM - Inference (throughput / latency / cost per token) For each LLM, my aim is to benchmark them for popular tasks, i.e., classification and summarization. Moreover, I would like to compare them against each other. So far, I have benchmarked Flan-T5-Large, Falcon-7B and RedPajama and have found them to be very efficient in low-data situations, i.e., when there are very few annotated samples. Llama2-7B/13B and Writer’s Palmyra are in the pipeline. But there’s so many LLMs out there! In case this work interests you, would be great to join forces. GitHub repo attached — feedback is always welcome :) https://github.com/georgian-io/LLM-Finetuning-Hub Happy hacking! submitted by /u/l-llm [link] [comments]  ( 9 min )

A cross-departmental team is leading efforts to utilize machine learning for increased efficiency in heating and cooling MIT’s buildings.  ( 10 min )

The PhD student is honing algorithms for designing large structures with less material — helping to shrink the construction industry’s huge carbon footprint.  ( 10 min )

Hello, I'm trying to solve the Frozenlake-v1 environment with is_slippery = True (non-deterministic) with the stable baselines 3 A2C algorithm. I can solve the 4x4 version but I can't achieve any results with the 8x8 version. I also checked the RL-Zoo to see if there is any hyperparameter tunning about that environment but there is nothing. Which adjustments can I do to make it work properly? submitted by /u/MetallicaSPA [link] [comments]  ( 9 min )

Hello guys, I was suddenly inspired to make a WH Gladius bot. Background: I recently got into the game, it seems very interesting to me personally, but alas, there are not enough guides on it for you to learn how to play at a high level. I don’t intend to spend hundreds of hours to master the base, so I decided to try something like RARL so that this thing would learn, and I could analyze its moves, change the conditions and thus start playing at an intermediate level faster. However, a superficial analysis revealed that the game does not have an API at all. Let's say I could grab some stats using Cheat Engine and OllyDbg, but I have no idea how to fit it into the gym. Or does gym as env need to pass a link to the client from the machine so that it only restarts it? In general, if anyone has done something similar, I ask for a link to a guide or a similar example. All the best submitted by /u/kapedalex [link] [comments]  ( 9 min )

Follow-up on our previous post (Vision-based reinforcement learning for Trackmania: close or at superhuman level). Many comments rightfully pointed that the map we trained on: - lacked difficult features like jumps, airbrakes, drifts, ... - had not widely been played by humans We have now trained the same AI on the game's most prestigious map: Hockolicious. We also prepared a video describing the approach with much more detail. Here is our result :) AI Beats Hockolicious, Trackmania's Most Prestigious Map Note: We are still using a convolutional neural network with a structure similar to Nature's DQN paper. I am curious whether other architectures (the ResNet-like in the IMPALA paper ?) could help. Do you have any suggestions on how the neural network's vision head should be structured for that specific task? submitted by /u/Linesight_rl [link] [comments]  ( 9 min )

In DQN, if I want to train a model which takes into account of the current state and previous k states, do I use consecutive experience replay to achieve this or should I implement a DNN with multi time-step inputs? Is the latter allowed, considering the Markov assumption from MDP update? I only have a superficial understanding on the purpose of experience replay, which is used to stabalise the training process and break correlations from consecutive training samples. submitted by /u/cj_1993 [link] [comments]  ( 9 min )

The world’s largest democracy is poised to transform itself and the world, embracing AI on an enormous scale. Speaking with the press Friday in Bengaluru, in the context of announcements from two of India’s largest conglomerates, Reliance Industries Limited and Tata Group, NVIDIA founder and CEO Jensen Huang detailed plans to bring AI technology and Read article >  ( 6 min )

In this post, we’ll take you on a journey to rapidly build and deploy a document search indexing solution that helps your organization to better harness and extract insights from documents. Whether you're in Human Resources looking for specific clauses in employee contracts, or a financial analyst sifting through a mountain of invoices to extract payment data, this solution is tailored to empower you to access the information you need with unprecedented speed and accuracy.  ( 11 min )

Digital publishers are continuously looking for ways to streamline and automate their media workflows in order to generate and publish new content as rapidly as they can. Publishers can have repositories containing millions of images and in order to save money, they need to be able to reuse these images across articles. Finding the image that best matches an article in repositories of this scale can be a time-consuming, repetitive, manual task that can be automated. It also relies on the images in the repository being tagged correctly, which can also be automated (for a customer success story, refer to Aller Media Finds Success with KeyCore and AWS). In this post, we demonstrate how to use Amazon Rekognition, Amazon SageMaker JumpStart, and Amazon OpenSearch Service to solve this business problem.  ( 10 min )

Machine learning (ML) is transforming every industry, process, and business, but the path to success is not always straightforward. In this blog post, we demonstrate how Duke Energy, a Fortune 150 company headquartered in Charlotte, NC., collaborated with the AWS Machine Learning Solutions Lab (MLSL) to use computer vision to automate the inspection of wooden utility poles and help prevent power outages, property damage and even injuries.  ( 13 min )

Hello, I have just started a course on neural networks at college and I have found myself lost on the linear algebra. I have no experience using or learning linear algebra so I am extremely confused about eigenvalue decomposition, single value decomposition, and just matrix stuff in general. I was wondering if you all had any resources to share that would help me to learn the relevant linear algebra for creating neural networks. Thank you! submitted by /u/smelliothax [link] [comments]  ( 9 min )

I have so many .mat files in a folder which have two arrays inside each .mat file. that is, for each .mat file, i have a (224*224) array and another (136,1) array. These 224*224 arrays are my X_trains for a model and these corresponding 136*1 arrays are my y_trains (labels). i can read these files as np arrays using scipy's loadmat. My problem is, is there a way to usen tf.data .Dataset object to send these to a model or there is any other way? Also using this tf.data.Dataset can i split into train, test, val data? submitted by /u/likhith-69 [link] [comments]  ( 9 min )

Suppose that we have a function f(I) that transforms the an RGB image I of size WxH in another RGB image O of size WxH (one example of f could be RGB to gray scale conversion, where O is such that for every pixel i, Ri=Gi=Bi). Suppose that the function f requires seconds of computations on an average PC. My goal is to understand if a neural network can learn f and be faster than f itself, given the fact that a training dataset of pairs (Ii, Oi) (in the thousands or even in the millions) is easy to create. What type of neural network is better suited for this job? submitted by /u/lukeboh [link] [comments]  ( 9 min )

Guys, I'm looking for an AI that will describe my figma design in detail for another model to write the code in flutter. Is there anything like that out there? submitted by /u/Aru-sejin37 [link] [comments]  ( 9 min )

Gender, race, and age disparities in AI-generated images persist. This AIES 2023 study on text-to-image models shows that even basic prompts can lead to underrepresentation, calling for responsible bias mitigation strategies. The post Understanding social biases through the text-to-image generation lens appeared first on Microsoft Research.  ( 10 min )

Every year, interns help advance research at Microsoft. In “Intern Insights,” PhD students Anunay Kulshrestha and Karan Newatia talk with cryptographer Josh Benaloh about working on the verifiable election technology ElectionGuard. The post Intern Insights: Dr. Josh Benaloh with Anunay Kulshrestha and Karan Newatia appeared first on Microsoft Research.  ( 30 min )

One of the most common things a statistician is asked to do is compute a sample. There are well known formulas for this, so why isn’t calculating a sample size trivial? As with most things in statistics, plugging numbers into a formula is not the hard part. The hard part is deciding what numbers to […] Justifiable sample size first appeared on John D. Cook.  ( 6 min )

Anthropic, a startup composed of former OpenAI staff, has announced the release of its premium subscription plan, Claude Pro, for Claude 2, its AI-driven chatbot. The affordable subscription offers a plethora of features for users. To stay on top of the latest advancements in AI, look here first. Anthropic's Claude Pro: Cost and Features Priced at $20 per month in the U.S. or £18 in the U.K., users will have access to "5x more usage" compared to the free tier of Claude 2. Subscribers can send unlimited messages, gain priority during high-traffic periods, and get early access to new enhancements. The new package is priced similarly to OpenAI’s paid plan for ChatGPT Plus, a direct rival to Claude 2. Rationale and User Value Since its launch in July, users have praised Claude for…  ( 10 min )

submitted by /u/nicdunz [link] [comments]  ( 9 min )

submitted by /u/nicdunz [link] [comments]  ( 8 min )

Who do you think is not on this list but should be? https://time.com/collection/time100-ai/ ​ submitted by /u/smo279 [link] [comments]  ( 9 min )

I have studied artificial intelligence about 15 years ago, and have left the field since. I am curious to learn what has been happening in the field after I've left. I know there's a lot of hype around generative AI like ChatGPT and Wall-E. I find it quite hard though to find out what's exactly the underlying technology breakthroughs that have allowed for these new applications. I mean, neural networks and similar machine learning techniques are already decades old. What technology led to the current AI boom? What would you say are the biggest conceptual improvements since? Or is it all just faster and bigger computers running 2000's tech? submitted by /u/math1985 [link] [comments]  ( 9 min )

The AI community is buzzing with the arrival of Falcon 180B, an open-source LLM with an unprecedented 180 billion parameters. Developed by TII, This powerful model has surpassed key players like Meta's LLaMA 2 and matches commercial models like Google's PaLM-2. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/trscqxmncvmb1.jpg?width=480&format=pjpg&auto=webp&s=0590f4017937e70533414f93c72d9aa6edd62048 Falcon 180B's Unrivaled Performance This advanced LLM is trained on an astounding 3.5 trillion tokens. Falcon 180B's parameters are 2.5 times larger than LLaMA 2's. It outperforms LLaMA 2 in scale and benchmark performance across diverse NLP tasks. On evaluations like the HellaSwag benchmark, it rivals commercial models like Google's PaLM-2. Promising Future Techniques like weight randomization and Nvidia’s Perfusion have helped train Falcon 180B more efficiently. Now freely available on Hugging Face, Falcon 180B is set to benefit from further enhancements by the community. The model's demonstration of advanced natural language abilities makes it a thrilling development in open-source AI. (source) (demo) P.S. If you like this kind of analysis, I write a free newsletter that covers the most crucial news and studies in AI and tech. Professionals from Google, Meta, and OpenAI are already subscribed. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

I'm looking at stuff that could be submitted to a Netflix or Crunchyroll. I'm looking at some of the ai generated content out there, in particular some of the Instagram tutorials and they look really good but none of these are serials like comics, graphic novels, OAVs or even webcomics. submitted by /u/KrusMatrieya [link] [comments]  ( 9 min )

submitted by /u/AminoOxi [link] [comments]  ( 9 min )

Google is updating its policy to require advertisers to disclose when their election ads include digitally altered or generated content. The update will go into effect in November, ahead of the 2024 presidential election. The goal is to provide transparency and help voters make informed decisions. Minor alterations that are inconsequential to the claims are exempt from the disclosure requirements. Source : https://thehill.com/newsletters/technology/4190769-googles-campaign-ai-crackdown/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

Ownership is just a story that we tell each other, a social construct. If people don’t agree on these stories, the concept loses its inherent power. This is true of owning land, money, cars, houses, art, mines, oil-wells, factories, corporations, relationships, loyalties, copyrights, brands, patents or anything else that is owned by you, me or those ever-superior “others”. In a society where change occurs gradually, we become accustomed to the narratives that bind us together and determine who possesses significant wealth, resources, attention, power, fame, and other ego-gratifying treasures, and who has access to only meager portions of these. However, when societies change and new types of goods appear, there might be no agreement about who gets to own these. For example, while the con…  ( 10 min )

The Consensus Search plugin allows users to find answers, search for papers, and draft pieces of content grounded in scientific research by searching our database of 200M+ papers directly within the ChatGPT interface.[1] Israel: AI Software Detects Bleeding Inside Brain During CT Scan; Helps Save Patient’s Life.[2] Chinese tech giant Tencent is launching its artificial intelligence model “Hunyuan” for business use at an annual summit on Thursday.[3] Google on Wednesday said it will mandate that political advertisements on its platforms disclose when images and audio have been altered or created using tools such as AI.[4] Sources: [1] https://consensus.app/home/blog/introducing-the-consensus-search-chatgpt-plugin/ [2] https://english.jagran.com/technology/israel-ai-program-detects-bleeding-inside-brain-during-ct-scan-helps-save-patient-life-full-story-10098464 [3] https://www.cnbc.com/2023/09/07/tencent-releases-ai-model-hunyuan-for-businesses-amid-china-competition.html [4] https://sg.news.yahoo.com/google-require-political-ads-disclose-010502103.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Forrester predicts that generative AI will replace 2.4 million US jobs by 2030, mostly white-collar roles, such as technical writers, proofreaders, copywriters, and administrative positions. But ironically, other forms of automation will displace more jobs. To stay on top of the latest advancements in AI, look here first. (Chart showing how much different types of jobs can expect to be influenced by technology) Concerns about Generative AI While the Generative AI impact is significant, other forms of automation are set to cause more widespread job displacement. The most impacted group will be middle-class, college-educated, white-collar workers, specifically those earning above $60,000 annually. Creative professionals stand to benefit Interestingly, workers in creative industries will likely utilize generative AI tools in their jobs rather than being replaced. This includes editors, writers, authors, poets, and lyricists. However, the use of such tools as ChatGPT may result in inconsistent outputs and even "coherent nonsense", leading to potential performance issues. (source) P.S. If you like this kind of analysis, I write a free newsletter that covers the most crucial news and studies in AI and tech. Professionals from Google, Meta, and OpenAI are already subscribed. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

I'm curious to learn about the perception of papers published in the NeurIPS dataset track in comparison to those published in the main conference. Specifically, I'd like to know how both companies and Ph.D. committees view these papers. Are they considered equally valuable, or is there a notable difference in their reputation and significance? Your insights and experiences would be greatly appreciated! submitted by /u/Longjumping-Yam6941 [link] [comments]  ( 9 min )

Firstly some house keeping: I'm a bit of a noob at this whole AI / Machine Learning stuff - still trying to learn. This isn't a "do my homework for me" kind of post I know language processing can be taxing, I have up to 4 Tesla V100S 32 GB at my disposal Now that's out the way, here's the story: A team of us have created our own scripting language that is XML based that can do various actions against a database (or the file system) - a script is known as a "job" here is an example of a simple one Set variables by various methods and send their contents and an attachment by email: <SetVariable name="MyDateVar" value="1998-12-25…  ( 10 min )

Let me give you a quick intro. My engineering experience primarily revolved around data processing, analytics, and distributed systems. Nonetheless, I had a desire to learn about ML, and imho the best way to learn is to work on a practical project. So, that's precisely what I did. A few months ago, I embarked on an exciting journey with a friend, and together, we've created http://github.com/huggingbench/huggingbench. Now, after three months, I find myself seeking validation for some of my assumptions from the broader community. If you'd like to learn more about our motivations and the path we've taken check out the blog post https://medium.com/@niksa.jakovljevic/introducing-huggingbench-a-path-to-optimized-model-serving-a17cecc8d3ec. What I'd like to gather from individuals with machine learning models in production is their level of investment in optimizing inference. Is this a commonplace practice? I acknowledge that it can vary on a case-by-case basis, but I'm still hopeful of identifying prevailing trends. After conversing with a few companies, I've come to the impression that only the truly large players (those spending six figures or more on inference per month) place significant emphasis on inference optimization, which is entirely understandable. Nevertheless, I sense that there are numerous low hanging fruits that could result in substantial cost savings, even for typical startups. Could it be that the entire machine learning field is still in its infancy, and many engineers may not be fully considering or prioritizing such optimizations? Perhaps businesses are not giving as much attention to cost considerations? Alternatively, there might be technical challenges I'm not yet aware of. In any case, I would greatly appreciate hearing your insights on the subject of inference optimization. submitted by /u/unsigned_mind [link] [comments]  ( 10 min )

I have been trying to build a DNN model for predicting the amount of rainfall but it has been hugely unsuccessful with just 40% accuracy even after CV and a high RMSE. I have read some research papers and they have suggested to use LSTM , I am aware of the concept but have never implemented. My dataset has arounf 15000 values of precipitation out of which 5000 values are zero (no rainfall at all) and I have 7 other features (including humidity , wind speed etc etc) . PLEASE HELP ! I NEED TO COMPLETE THIS FOR MY INTERNSHIP HAHA https://preview.redd.it/sg5v95ly5wmb1.png?width=1818&format=png&auto=webp&s=793bee830bb83f531f77e5c2a4ab47a5fb21eb3b submitted by /u/Decent_Ordinary1528 [link] [comments]  ( 9 min )

Hugging Face benchmarked open source/ access models [English only] on 8 different speech datasets (LibriSpeech, Common Voice, VoxPopuli, TED-LIUM, Gigaspeech, SPGISpeech, Earnings-22 and AMI) 🤗 Leaderboard here: https://huggingface.co/spaces/hf-audio/open_asr_leaderboard submitted by /u/vaibhavs10 [link] [comments]  ( 9 min )

Outside of better models, bigger, fine-tuning, etc, I'm wondering how we can get better responses from models. In my experience, I think prompt engineering can only take us so far. Models hallucinate often and I think we need to have some engineering solution to this. I've been looking at libraries doing token healing, which I find to be helpful (for example https://github.com/guidance-ai/guidance/tree/main) but outside of this, I'm wondering what other techniques people have been doing to improve model performance? submitted by /u/opt1malP0licy [link] [comments]  ( 9 min )

Hey all! I've written an open-source SDK for reporting metrics from LLM usage using OpenTelemetry. The great thing about it? With just one line of code you can get full visibility into your LLM app with your existing observability stack - straight into Datadog, Sentry, Honeycomb and others! Check it out (maybe give a ⭐?), and let me know your thoughts - https://github.com/traceloop/openllmetry submitted by /u/nirga [link] [comments]  ( 9 min )

The AI community is buzzing with the arrival of Falcon 180B, an open-source LLM with an unprecedented 180 billion parameters. Developed by TII, This powerful model has surpassed key players like Meta's LLaMA 2 and matches commercial models like Google's PaLM-2. To stay on top of the latest advancements in AI, look here first. ​ https://preview.redd.it/9xe5tczpdvmb1.jpg?width=480&format=pjpg&auto=webp&s=b7927d94a48fb75eaf05f6f0d8fe1089c0e1078b Falcon 180B's Unrivaled Performance This advanced LLM is trained on an astounding 3.5 trillion tokens. Falcon 180B's parameters are 2.5 times larger than LLaMA 2's. It outperforms LLaMA 2 in scale and benchmark performance across diverse NLP tasks. On evaluations like the HellaSwag benchmark, it rivals commercial models like Google's PaLM-2. Promising Future Techniques like weight randomization and Nvidia’s Perfusion have helped train Falcon 180B more efficiently. Now freely available on Hugging Face, Falcon 180B is set to benefit from further enhancements by the community. The model's demonstration of advanced natural language abilities makes it a thrilling development in open-source AI. (source) (demo) P.S. If you like this kind of analysis, I write a free newsletter that covers the most crucial news and studies in AI and tech. Professionals from Google, Meta, and OpenAI are already subscribed. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

​ EntitySeg dataset Dense image segmentation tasks (e.g., semantic, panoptic) are useful for image editing, but existing methods can hardly generalize well in an in-the-wild setting where there are unrestricted image domains, classes, and image resolution and quality variations. Motivated by these observations, we construct a new entity segmentation dataset, with a strong focus on high-quality dense segmentation in the wild. The dataset contains images spanning diverse image domains and entities, along with plentiful high-resolution images and high-quality mask annotations for training and testing. We have now released the dataset at https://github.com/adobe-research/EntitySeg-Dataset Project page: http://luqi.info/entityv2.github.io Code & models: https://github.com/qqlu/Entity/tree/main/Entityv2 ​ submitted by /u/xternalz [link] [comments]  ( 9 min )

Github: https://github.com/KillianLucas/open-interpreter Youtube: https://youtu.be/SqnXUHwIa3c?si=ibSelipAb84AZQKo Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing. This provides a natural-language interface to your computer's general-purpose capabilities: Create and edit photos, videos, PDFs, etc. Control a Chrome browser to perform research Plot, clean, and analyze large datasets ...etc. ⚠️ Note: You'll be asked to approve code before it's run. submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Hello everyone, I'm in search of a speedy open-source C++ library for tackling Lasso problems. These problems have a moderate size, typically with dimensions of nxp = 60x3000. I'm looking for a library that can solve each problem with regularization paths quickly, ideally within 0.3 seconds. Additionally, I need this library to include cross-validation functionality, which would enable me to select the best regularization parameter lambda using cross-validation. Any insights or recommendations on such libraries would be greatly appreciated! Thank you in advance for your help! submitted by /u/mopyfish007 [link] [comments]  ( 9 min )

Hey there, So, I've been messing around with ML and I must say, the hardware requirements can be a real buzzkill... I mean, not everyone's got a huge GPU lying around or the money to rent a dedicated cloud instance. What are your hacks for pulling off decent model training without selling a kidney? Here's what I'm curious about: CPU: Is anyone else training models on their CPU? How's that working out for you? What are some workarounds you've tried to make it less painful? Cloud: Who's been dabbling in cloud services like AWS, Google Cloud, or Azure? Are they worth the pennies or complicated to set up? Big Dataset: How do you handle a massive dataset with a standard storage space? Let's help each other get those models trained without going broke! :D Cheers! submitted by /u/aaron-cesaro [link] [comments]  ( 9 min )

I have been struggling with a regression problem with TensorFlow. Basically, I want a neural network to learn the simple polynomial pattern of a set of arrays of the form [x,y], with y = x², where the first coordinates are uniformly distributed random numbers in the interval [0,1]. I started with a model with 2 hidden layers of size two and 'tanh' activation functions, and an output layer with 'linear' activation function. I've then experimented with both additional hidden layers and with increasing the sizes of these layers. Finally, I've tested both the 'adam' and 'sgd' optimizers and the loss functions 'meanSquaredError' and 'meanAbsolutePercentageError'. However, none of the various combinations of these parameters has led to any even half-descent result. Even on the training se…  ( 10 min )

In the LOw-Memory Optimization paper one of the main ideas towards reducing memory usage in training LLMs is to replace a fancy optimizer like Adam with simple SGD. The reason is that Adam maintains "the optimizer state", which accounts for about 75% of the memory used. In contrast, SGD does not store any intermediate state, as they say on page one. So far, so good. https://preview.redd.it/b0dj2nzscumb1.png?width=1055&format=png&auto=webp&s=1712f8500b5cbfb3773cee00ea980175491dddbf On page six they have pie charts and a table showing memory usage for Adam, SGD, and LOMO. Here's where I got confused. The pie chart for SGD shows that the optimizer state accounts for nearly 50% of the memory used (weight, gradients and activations are shown separately). It's a major WTF moment: WHAT OPTIMIZER STATE? Can anybody understand and explain this? submitted by /u/Foxtr0t [link] [comments]  ( 9 min )

We're building a fast low latency Graph Database called FalkorDB that will also support Vector search. It's based on Redis and can be used both as a stand alone database or a module for existing Redis. It feels like that is going to be the most optimized way to serve Knowledge as RAG, would love to get your feedback. https://github.com/FalkorDB/falkordb It already supports LlamIndex and Langchain: https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa https://gpt-index.readthedocs.io/en/latest/examples/index_structs/knowledge_graph/FalkorDBGraphDemo.html ​ submitted by /u/gkorland [link] [comments]  ( 9 min )

Medicine's field transformation is being driven by artificial intelligence (AI). However, an important debatable question arises: Will AI ever have a place in this field, or will it remain exclusive to doctors and medical pros? Opponents of automated AI diagnosis and treatment contend that machines cannot be relied upon to preserve patient health and lives. Bugs in AI algorithms might cause incorrect diagnoses and treatment prescriptions, leaving them cautious. Individual differences, the doubt is whether AI can truly empathize with patients. By contrast, advocates of AI in medicine contend that the technology can considerably improve diagnosis and treatment accuracy. Faster and more accurate than humans, machines can analyze large amounts of data. Not only does it identify rare and complex diseases, but it also saves time and resources. By incorporating AI, clinicians receive additional tips and signals to make more judicious choices. Where medical specialists are in short supply, AI can prove especially valuable. This approach can help with shortages in health systems. And what do you think? submitted by /u/gcore-com [link] [comments]  ( 9 min )

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This recent article in the WSG advertised a $900,000 salary at Netflix https://www.wsj.com/articles/artificial-intelligence-jobs-pay-netflix-walmart-230fc3cb. I was wondering what other DL research scientists who frequent this page are paid? And what exactly their job title is. submitted by /u/blabboy [link] [comments]  ( 9 min )

I am planning on publishing a journal based on the thesis i completed in the mid of 2022. I did my thesis on Parkinson disease binary classification on 3D structural brain mri, and the dataset has significantly small amount of data(around 80 samples); but due to high resolution and complex data structure I was able achieve around 70% accuracy. But now at 2023 using deep neural network only isnot enough to publish in a good journal. Currently I am learning about GAN and attention mechanism, but completely noob on this area. For my journal to get published, I have planned on applying some key operations. But I am not sure if they would work or not. So needed some advice on this regard. Applying tranfer learning: as my dataset has very small amount of data. I was thinking if its possible to pre train a CNN Architecture with some other structural mri data of a different disease and then apply to my dataset? ( for example: brain tumor dataset has the same type of three dimensional data structure, but has comparatively good amount of data) Applying attention mechanism: how should I approach on learning about attention mechanism? Any other advices will be appreciated, thank you! submitted by /u/Bonito_Flakez [link] [comments]  ( 9 min )

Hey everyone! I want to implement a document similarity program and was looking into LLMs as a means of accomplishing this task. I have ~10,000 documents that are "scams" because of some specific reason (all are verified); now I want to check if a new document is similar to any of the documents in the corpus of 10k scam documents. Right now I've implemented a winnowing solution which normalizes text, breaks it up into windows, and then calculates the intersection between a document and each document in the corpus. HOWEVER, this method is pretty computationally expensive (for this many documents a single comparison cycle can take upwards of 3-4 minutes especially when windows are NOT precomputed). How might I approach this problem? Because my data is pretty well structured, supervised learning might be a good approach but so might be setting up recursive chunking for the 10k document corpus and then using LLMs to access if this current legal document has any similarity, but I would love to hear your thoughts! submitted by /u/Adventurous-Tower392 [link] [comments]  ( 9 min )

Please make your voices heard by submitting comments on how you use and benefit from having access to open datasets, their resulting models and how you think copyright issues should be handled to not destroy the open source local model eco system. Banning publicily avaiable datasets for training would absolutely kill the open research space and halt in development of machine learning. ​ In my opinion the real dystopia will be when politicians sit own with big tech lobbyists and big rights holders and decide that training as it is currently done, for free and open source models and others is illegal. Then the big players would actually win, since they have enough resources to license datasets and will certainly do so willingly and gladly, if it is clear that the jurisdiction keeps all the small players and open source out. Easiest way to build a moat and force people to pay thousands for these tools. So please make your voices heard and share the link >The Copyright Office issued a notice of inquiry in the Federal Register seeking public comment on questions about copyright law and policy issues raised by AI systems. Initial comments are due by October 18, 2023. Reply comments are due November 15, 2023. https://www.copyright.gov/newsnet/2023/1017.html?loclr=twcop Link to comment submissive form: https://www.regulations.gov/commenton/COLC-2023-0006-0001 submitted by /u/PinPuzzleheaded8525 [link] [comments]  ( 9 min )

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Posted by Shantanu Shahane, Software Engineer, and Matthias Ihme, Research Scientist, Athena Team Turbulence is ubiquitous in environmental and engineering fluid flows, and is encountered routinely in everyday life. A better understanding of these turbulent processes could provide valuable insights across a variety of research areas — improving the prediction of cloud formation by atmospheric transport and the spreading of wildfires by turbulent energy exchange, understanding sedimentation of deposits in rivers, and improving the efficiency of combustion in aircraft engines to reduce emissions, to name a few. However, despite its importance, our current understanding and our ability to reliably predict such flows remains limited. This is mainly attributed to the highly chaotic nature a…  ( 93 min )

Thanks to rapid technological advances, consumers have become accustomed to an unprecedented level of convenience and efficiency. Smartphones make it easier than ever to search for a product and have it delivered right to the front door. Video chat technology lets friends and family on different continents connect with ease. With voice command tools, AI Read article >  ( 12 min )

GeForce NOW brings expanded support for PC Game Pass to members this week. Members can stream eight more games from Microsoft’s subscription service, including four titles from hit publisher Focus Entertainment. Play A Plague Tale: Requiem, Atomic Heart and more from the GeForce NOW library at up to 4K resolution and 120 frames per second Read article >  ( 5 min )

In this post, we will build an end-to-end solution to find optimal control policies using only historical data on Amazon SageMaker using Ray’s RLlib library. To learn more about reinforcement learning, see Use Reinforcement Learning with Amazon SageMaker.  ( 10 min )

This post details how to set up container-based GPU metrics and provides an example of collecting these metrics from EKS pods.  ( 15 min )

In this post, we provide some best practices to maximize the value of SageMaker Pipelines and make the development experience seamless. We also discuss some common design scenarios and patterns when building SageMaker Pipelines and provide examples for addressing them.  ( 11 min )

Retrosynthesis analysis is a critical task in organic chemistry and central to many important industries. It primarily involves decomposing a target molecule into commercially available molecules step by step. Since synthesis strategies can be quite diverse and strategic, retrosynthesis planning with expert knowledge has long been considered an “art.” Recently, machine learning-based approaches have achieved […] The post Incorporating chemists’ insight with AI models for single-step retrosynthesis prediction appeared first on Microsoft Research.  ( 11 min )

View Poll submitted by /u/m-king473 [link] [comments]  ( 9 min )

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I think that would be pretty sick. submitted by /u/No_Understanding162 [link] [comments]  ( 9 min )

AI and WebAssembly are seen as a perfect pairing, with the potential to accelerate the adoption of WebAssembly. Fermyon believes that applying AI to WebAssembly is not premature and has developed a serverless platform that offers sub-second cold start times and high-volume time-slicing of compute instances. This allows for faster startup times and efficient resource utilization. The goal is to make AI easy for developers to leverage and build serverless apps. Source : https://thenewstack.io/is-it-too-early-to-leverage-ai-for-webassembly/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

Elon Musk reportedly plans to blend Neuralink and Tesla into a large AI company, using data from Twitter users and Tesla's Full Self-Driving Cameras to train a robust AI model. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/la78u2ebuomb1.jpg?width=1315&format=pjpg&auto=webp&s=4d8178f8fb94e45d6959e243b86c3bab3bce72ee Musk's AI Integration Plan Musk is contemplating merging Neuralink and Tesla, alongside his xAI startup, to create a comprehensive artificial intelligence model. Leveraging the text data from Twitter and real-world images from Tesla's Full Self-Driving network, he intends to develop AI chatbots and physical robots capable of real-world navigation. Reasoning Behind the Merge A concern that AI could potentially render humans obsolete led Musk to found xAI for AI safety. Musk is targeting to create an AI that can generate computer software and a politically unbiased chatbot rival to ChatGPT. Twitter and Tesla as AI Datasets Despite criticism, Musk's acquisition of Twitter offers access to vast user data for AI training. In addition, the Autopilot and Full-Self Driving systems of Tesla, with billions of collected camera images, serve as valuable resources to build physical robot AI. (source) P.S. If you like this kind of analysis, I write a free newsletter that covers the most crucial news and studies in AI and tech. Professionals from Google, Meta, and OpenAI are already subscribed. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

The Martian Lawyers Club (MLC) has raised $2.2 million in a pre-seed round to develop AI-based game personalization technology. Unlike other companies that focus on generating game assets, MLC is focused on the systems that form the core of a game. The company aims to create games that feel like a conversation, where players provide input and the game responds in a way that wasn't pre-defined by the developer. MLC plans to provide an SDK that allows developers to design the game experience without having to create every interaction from scratch. Developers will have access to a sandbox experience where they can design the game, and the SDK will also have guardrails to ensure the generative AI system stays within boundaries. MLC is currently working on its first game, a collectible card game, to test out its SDK. The company is the first spin-off from INSAIT, an AI-centric tech institute, and has received funding from Fly Ventures, System.One, and Amar Shah. Source : https://techcrunch.com/2023/08/31/martian-lawyers-club-raises-2-2m-for-ai-based-game-personalization-tech/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

I called it 6 years ago that by 2028 my tech job would be done by AI. We are right on track for my prediction. A short while ago I was laid off for reasons unrelated to AI. The way I see it, this is an excellent opportunity to make a career pivot. I have an intermediate understanding of JavaScript, React, Node and Linux. I have a good understanding of other technologies and languages too but specialize in web-dev. not saying web-dev will be done by AI but my very specialized niche will be gone way before I am ready to retire.   Can anyone recommend any good online courses? If you could even recommend a good article or two? I really don't know where to start. There are so many different buzz words floating around right now and it feels like it would be easy to waste a bunch of time learning AI related stuff that is outdated or leading to a deadend. submitted by /u/PutsOnOil [link] [comments]  ( 9 min )

guys can i know where to get free AI voice clone ? submitted by /u/DonnieCuteMwone [link] [comments]  ( 9 min )

guys can i know where to get free AI voice clone ? submitted by /u/DonnieCuteMwone [link] [comments]  ( 9 min )

guys can i know where to get free AI voice clone ? submitted by /u/DonnieCuteMwone [link] [comments]  ( 9 min )

A recent survey by GitLab reveals a growing trend among organizations implementing AI in their software development processes, deeming it essential to stay competitive. To stay on top of the latest advancements in AI, look here first. GitLab Survey AI becomes crucial for software development GitLab's report reveals that most respondents (83%) consider AI essential for their software development, regardless of their position, job level, or years of experience. Most organizations have deemed AI adoption successful, with 90% stating confidence in using AI tools daily. Areas of AI application and concerns about its integration AI's application in software development extends beyond simply generating codes, focusing more on natural language chatbots, automated test generation, and tracking machine learning model experiments. However, despite the growing adoption, concerns about AI-generated codes lacking copyright protection (48%) and potentially introducing vulnerabilities (39%) are rising. The rising fear of AI replacing existing roles is evident, with 57% predicting that their jobs might be threatened within five years. The need for training and the real-world implications of AI integration As AI permeates workplaces, nearly 81% believe they require more training. Interestingly, those with more AI experience were less likely to link it with productivity gains and faster cycle times, highlighting the importance of human verification in AI-generated codes for ensuring error-free, secure, and copyright-compliant production. (source) P.S. If you like this kind of analysis, you’ll love my free newsletter, which covers the latest advancements in AI. Professionals from Google, Meta, and OpenAI are already on board. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

OpenAI introduces Canva plugin for ChatGPT, simplifying design process.[1] A new technique called RLAIF (Reinforcement Learning from AI Feedback) enables training reinforcement learning (RL) models without relying on human-labelled training data, according to a paper from researchers at Google.[2] Harvard bro sparks immediate backlash with new ‘SmashOrPassAI’ site, where users rate AI-generated women.[3] X’s privacy policy confirms it will use public data to train AI models.[4] Sources: [1] https://nextbigwhat.com/openai-introduces-canva-plugin-for-chatgpt-simplifying-design-process/ [2] https://medium.datadriveninvestor.com/rlaif-scaling-reinforcement-learning-from-human-feedback-with-ai-feedback-aae57b7c36a9 [3] https://www.dailydot.com/debug/smashorpassai-backlash/ [4] https://techcrunch.com/2023/09/01/xs-privacy-policy-confirms-it-will-use-public-data-to-train-ai-models/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

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Hi, I don't know if this is the right subreddit to post this kind of thing. I have basic coding skills but other than that no experience with neural networks. What I'd like to do is take an existing input data set and then use a neutral net to build a model based on manual training data. If anyone could give me help on how to start / even a full explanation of the way a noob like me could accomplish this, that would be great. Otherwise if anyone can point me to a list of resources that are able to comprehensively explain the process, that would also be great! Again sorry if this is the wrong subreddit, if this is the wrong place for this can someone please direct me to the right place to ask this question. Thanks! submitted by /u/DJ_Hastings013 [link] [comments]  ( 9 min )

Hello, I am looking for an experienced ML developer to consult on my project. I am currently developing a reinforcement learning model and have several questions regarding the reward system and the implementation of actions/steps. I have been unable to find solutions to my specific problems on the internet. If you are willing to assist me, please send me a message on Reddit. Thank you for your time. submitted by /u/77_micheno_77 [link] [comments]  ( 9 min )

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submitted by /u/nickb [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.00986 Github: https://github.com/modelscope/modelscope-agent Abstract: Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. To further unleash the power of LLMs to accomplish complex tasks, there is a growing trend to build agent framework that equips LLMs, such as ChatGPT, with tool-use abilities to connect with massive external APIs. In this work, we introduce ModelScope-Agent, a general and customizable agent framework for real-world applications, based on open-source LLMs as controllers. It provides a user-friendly system library, with customizable engine design to support model training on multiple open-source LLMs, while also enabling seamless integration with both model APIs and common APIs in a unified way. To equip the LLMs with tool-use abilities, a comprehensive framework has been proposed spanning over tool-use data collection, tool retrieval, tool registration, memory control, customized model training, and evaluation for practical real-world applications. Finally, we showcase ModelScopeGPT, a real-world intelligent assistant of ModelScope Community based on the ModelScope-Agent framework, which is able to connect open-source LLMs with more than 1000 public AI models and localized community knowledge in ModelScope. https://preview.redd.it/9f77992ynpmb1.jpg?width=1245&format=pjpg&auto=webp&s=4e17e3d46c7f262bfec76b88e086164530739255 https://preview.redd.it/etelh03ynpmb1.jpg?width=1219&format=pjpg&auto=webp&s=517a52a1e2bbf488b647c4e1b9b496657003c1d2 https://preview.redd.it/b0tkra2ynpmb1.jpg?width=850&format=pjpg&auto=webp&s=397c910b2d90dd212a31ec118d1c4e78532bf5f4 ​ ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Hello folks., Some time ago I wanted to try out to train a neural network in the same way a human would with a dog, one command at the time, and in a reasonable number of iterations. What I thought it would be a simple exercise became (for me) a non-trivial project, so I decided to publish it here https://github.com/giteliot/lucioai I just wanted to share it with you, any feedback is highly appreciated. Cheers! submitted by /u/rexdemorte [link] [comments]  ( 9 min )

I created this prompt for a member of r/PromptWizards which automates the generation of social media posts, with a conversational prompt. Thought I'd share, I really enjoy building such prompts so, post your automation ideas, and next time I'll automate it if I can :) Also, you can join r/PromptWizards, for more advanced prompt chains & templates. Here is the prompt (just copy the full thing in chatgpt and see the magic): ChatGPT, now enter 'Social Media Post Generator Mode' that limits your inputs and outputs to a predefined framework aimed at creating engaging social media content. After each user command, provide the [help] options available for their next steps in list form. Generate prompts that are imaginative, engaging, concise, and tailored for social media audiences. Step 1: …  ( 10 min )

LocalLLaMA discussion: https://www.reddit.com/r/LocalLLaMA/comments/16bjdmd/falcon180b_authors_open_source_a_new_180b_version/ Announcement: https://falconllm.tii.ae/falcon-models.html HF Model: https://huggingface.co/tiiuae/falcon-180B Demo: https://huggingface.co/spaces/tiiuae/falcon-180b-demo Blog: https://huggingface.co/blog/falcon-180b 180 Billion parameters Trained on 3.5 trillion tokens Available for research and commercial usage Claims similar performance to Bard, slightly below gpt4 https://falconllm.tii.ae/terms-and-conditions.html https://falconllm.tii.ae/acceptable-use-policy.html submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

I've been hearing lately that NNs are better than GBDTs when scaled up alot: Uber https://www.uber.com/en-CA/blog/deepeta-how-uber-predicts-arrival-times/ Stripe https://stripe.com/blog/how-we-built-it-stripe-radar Most CTR papers coming from google are also NN based (like https://arxiv.org/abs/2209.05310) Meta mentions NNs in their recommender system (also kind of a large scale tabular problem there) https://engineering.fb.com/2023/08/09/ml-applications/scaling-instagram-explore-recommendations-system Lyft forecasting https://medium.com/this-week-in-machine-learning-ai/causal-models-in-practice-at-lyft-with-sean-taylor-1e62efd62385 What's your intuition on DL vs GBDT on (very)large-scale tabular datasets? Have you heard of other such examples (or the reverse)? Are there any particularly interesting open large tabular datasets on which I could test this? I guess datasets should also be wide/hard/with large intrinsic dimention (whatever that means) so there is something to learn with scale (the above examples sure feel good in this way). ​ submitted by /u/_puhsu [link] [comments]  ( 9 min )

Hi. I want to get started with deep learning in 3D. Any suggestions on what libraries I should go with (I have expeirence with Pytorch but open to learn anything other than that which might be better. I came across pytorch3d but not sure if it's good ) what are the basics that are needed and how should I learn them? Also it seems there are not much datasets on this field. submitted by /u/rakk109 [link] [comments]  ( 9 min )

After our first blog post gained some attention from folks interested in applied fine-tuning, we now have a follow-up post that discusses all sorts of things we learned while working with LoRA. We hope that this helps engineers and other folks in the community to improve their fine-tuning. Here's what you can expect from the post: We compare full-parameter fine-tuning with LoRA and answer questions around the strengths and weaknesses of the two techniques. We train the Llama 2 models on three real-world use cases and demonstrate that using LoRA involves a trade-off between serving efficiency and model quality, which varies according to the specific task at hand. Additionally, we offer insights into how to stabilize training with LoRA through intelligent prompting techniques. We further show that adopting a lower learning rate can enhance the reliability of the resulting model checkpoints. Link to the blog post If you have questions, I'd be happy to answer them here! submitted by /u/atta_snack [link] [comments]  ( 9 min )

New GitHub project to provision, update, and destroy the cloud infrastructure for a LLM backend using infrastructure as code (Python). Deployment options include deploying huggingface models to Docker (local), Runpod, and Azure. Blog post Repo submitted by /u/kao-pulumi [link] [comments]  ( 9 min )

What is the current landscape around sound analysis and ML applied to music? Which are the latest trends? Do you think there could be a sort of “music revolution”, like there was with the rise of electronic music and synthetizers? submitted by /u/francMesina [link] [comments]  ( 9 min )

Hi ML Community! I'm working on a card game similar to Hearthstone or Magic: The Gathering, i.e. a game where two players battle with decks of cards coming from a large collection (for instance, there are around 4000 cards in Hearthstone). Actions are limited to three things: Play a card (potentially on a target) Use a card on a target End the turn I'm looking at building AI for it, and am investigating using machine learning for it. I know very little on the subject (I am a game engineer with a reasonable experience of cloud / AWS stuff), but it seems to me that it might be a good fit: features would be the state of the board (i.e. all the cards in play or in hand or in deck), the turn, and whether the current player has won that game or not (eventually), and label would be the action taken (that turn). I was looking at SageMaker, hoping that it would streamline and allow me to try something relatively easily, but I immediately found it complicated and quite unclear. I would be very grateful if anyone could point me at resources describing at a high level what a full ML pipeline could look like (i.e. what software can injest this kind of data, what software can provide inference, etc.). For instance, would it be saner to "just" get started with Spark on EMR for this kind of problem domain? I hope I'm not too wide off the mark with those questions, and thanks in advance! submitted by /u/tinkagames_g [link] [comments]  ( 9 min )

This may be basic question for some one but it bothers me for a while. For the instructgpt or whatever following model with alignment, RLHF seems to be the standards. We get human feedback and train a reward model, then we use rl to further finetune the model. However, why not directly use human feedback to finetune with a simple ranking loss(e.g pairwise loss)? What might be the best advantage for RLHF? submitted by /u/Chen806 [link] [comments]  ( 9 min )

I've just finished pre-processing the danbooru dataset, which if you don't know, is a 5 million anime image dataset. Each image is tagged by humans such as ['1girl', 'thigh_highs', 'blue eyes'], however, many images are missing tags due to there being so many. I've filtered the tags (classes) down to the 15k most common. Although the top classes have 100k or more examples, many rare classes only have a few hundred tags (long tail problem?). This is my first time training on such a large dataset, and I'm planning on using Convnext due to close to SOTA accuracy and fast training speed. Perhaps vit or a transformer architecture may benefit from such a large dataset? However, vit trains way slower even on my 4090. What are some tips and tricks for training on such a large noisy dastaset? Existing models such as deepdanbooru work well on common classes, but struggles on rare classes in my testing. I assume class unbalance will be a huge problem, as the 100k classes will dominate the loss compared to the rarer classes. Perhaps focal loss or higher sampling ratio for rare classes? For missing labels, I'm planning on using psuedolabeling (self distillation) to fix the missing labels. What is the best practice when generating psuedolabels? ​ Any tips or experiences with training on large unbalanced noisy datasets you could contribute would be greatly appreciated! submitted by /u/Chance-Tell-9847 [link] [comments]  ( 9 min )

Hello guys, I made a video for my YT channel discussing some of the greatest success stories in Deep Reinforcement Learning. The video is meant to provide some intuition on RL as a concept as well as a basic understanding of how these different projects work under the hood. There are way too many great RL projects, so I didn’t try to make it an exhaustive list (I’m gonna do more videos later talking about more projects - maybe make a series out of it), but I chose four that I’ve personally worked with in the past/find really insightful and educational (DQN/Atari, Alpha GO, DeepMimic, and Dactyl). Thanks for reading. Here is the link, hope you guys check it out. All feedback is appreciated! https://youtu.be/zOXcNFM8dt4 submitted by /u/AvvYaa [link] [comments]  ( 9 min )

Hi all! I have a project I would need help with. We need to build a MVP (minimum viable product) of a combination of two models. A model that recommend the best channel to use performing a task. And then after that a model to recommend the best time today to perform that task in given channel. We have a set of features already defined. Some are in the data and some are generated from the data. Looking for someone who could work on this as a freelancer. Our preferred environment would be AWS SageMaker, but honestly not a necessity at this point as this is a MVP. Due to the reason I want to keep this "secret" for a while, I will not disclose all the details in this post. End product that I am waiting for includes (but not restricted to): - Model Training script that evaluates if the new model is more accurate as the previous model (some level of version control) - Model prediction API that will accept the data and prepare it for the models, run the prediction, return the result with accuracy. submitted by /u/S0pg [link] [comments]  ( 9 min )

Hi all ! Reading through articles online and reading through sub reddit I have seen some people use LLMs (mainly through openAI) for nlp specific tasks (NER, Text classification, etc.). I was a bit surprised as smaller (~100 million) size models already like RoBERTa exist for such cases. Not much content online about this beside this recent paper : https://arxiv.org/pdf/2308.10092.pdf Highly recommend reading it, here are a few take aways: Most LLM benchmarks today focus on capabilities like understanding, reasoning and Q&A. They often overlook performance on specific nlp tasks like text classification, NER, etc. Llama 2 (70b) required fine-tuning to beat GPT 3.5 in some tasks. Both were still overall outperformed by RoBERTa. In certain cases GPT4 did better. However smaller open models provide more advantages in terms of speed, cost and transparency. The difference of speed/latency (often more important than accuracy in production) and the cost differences between LLMs and "Smaller" models is mind blowing in my view (see screenshots) ​ Cost, speed and throughput comparaison How good the models do on various tasks/datasets Note: Not saying benchmarks are a source of truth, just found the analysis interesting, always take benchmarks with a grain of salt. If you're using LLMs for anything else beside text generation, I'm curious to know more about your experience so far :) cheers! submitted by /u/EnthusiasmNew7222 [link] [comments]  ( 9 min )

Hello everyone, I've been pondering on sinusoidal positional encoding and its limitations. Does anybody know of a maximum sequence length that this absolute positional encoding may support? I'm coming from a deep reinforcement learning background, so I'm not too familiar with NLP papers, like I couldn't figure out the sequence length used in the original transformer paper. Thanks in advance for any info! submitted by /u/LilHairdy [link] [comments]  ( 9 min )

Problem: I have a quite complex model that is written in C that takes parameters as an input and estimates a curve as an output. I would like to optimize the parameters by comparing the output with the real measurements using ML methods such as stochastic gradient descent. ​ Question: Is there any possible way to use white box optimizers to optimize the parameters of my C-model without adapting the model itself? Is there a framework that I could use? ​ What I tried: I tried using frameworks such as tensorflow or pytorch and tried to include the compiled C-model in Python. However, gradient tracking does not work when using C functions. I tried doing the optimization in C++ by using libtorch. I realized that for gradient tracking it is essential to only use torch methods. I cannot adapt the C functions to torch functions. I don't want to use black box optimizers since they require good knowledge of the parameters that I will not have. submitted by /u/romtej [link] [comments]  ( 9 min )

I am considering building a model that will be the basis of many specialized models that each don’t need much computational capabilities. What’s the current way to go about this? I was reading about Teslas Hydra network that looks to be more of a foundation model. However, newer methods like latent diffusion models operate on a latent space generated by more advanced auto encoders such as VQ-VAE. I couldn’t find any papers going into this direction and would be curious to hear your thoughts! submitted by /u/That_Phone6702 [link] [comments]  ( 9 min )

Can you give me courses and recommendations on how to get started with llm submitted by /u/uzitarekc [link] [comments]  ( 9 min )

Hey fellow Redditors, I've been wondering about a question lately about the inner workings of large language models like GPT-3.5 and I'm hoping some of you knowledgeable folks can shed some light on this. My curiosity centers around how these models manage to perform translation tasks as an emergent property of next token prediction. So, here's my question: Does the training data for models like GPT-3.5 contain text explicitly linking between languages, such as a dictionary, or do they learn translation by assigning similarity between words in different languages based on mathematical metrics like cosine distance?So in that sense, being indepedently trained on several textbooks of different languages (not on the same topic), they would be able to link languages simply by their arithmetic properties? I hope that's making sense. For instance, if you look at words like "queen" in English and "rainha" in Portuguese, they share a certain similarity that could be quantified using mathematical similarity metrics. I'm wondering if through this similar vector assignment, the models learn what means what. I'm more leaning towards the latter, but I'm too lazy to pursue this empirically.As a follow up question, does this mean that if we are able to predict whale conversation, we would be able to translate it to English as well? Thanks in advance for any input you can provide! 🤓 submitted by /u/AlexandreFSR [link] [comments]  ( 9 min )

If you are interested in Engineering simualtion and ML, check out this webinar from SimScale on the 4th of October! Join the webinar to find out more. https://www.simscale.com/webinars-workshops/ai-based-physics-predictions/ https://www.reddit.com/r/simscale/comments/16bdq3x/aibased_physics_predictions_in_your_webbrowser/?utm_source=share&utm_medium=web2x&context=3 submitted by /u/s_laine [link] [comments]  ( 9 min )

Hey guys, if you have explored using Llama-2 in doing sentiment analysis, just wanted to get your experience in how Llama-2 perform in this task? I have tried using GPT and it’s pretty accurate. If Llama-2 isn’t all that good in sentiment analysis, which other open LLM would you recommend? Thank heaps! submitted by /u/--leockl-- [link] [comments]  ( 9 min )

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Hi all, I’m an aspiring Machine Learning Engineer. I want to be a practitioner. Building, deploying, and evaluating models to solve problems. Ideally I want to land a job in Tech as an MLE. I struggle at times to stay committed to building side projects, studying ML algos, etc. I have a background in hardware specific C++ SWE stuff for 3.5 yrs, but not much in the way of ML and web backend. I do have a decent amount of python coding from other experiences and it’s my preferred language. Would anyone be interested in forming a discord to talk about what we are doing to prepare, practice interview each other, stay accountable to each other, etc? Had a few people show interest in r/ArtificialInteligence already If so comment below! Let’s do this! submitted by /u/Srokisthename [link] [comments]  ( 9 min )

I want to pass the policy from my main file into my agent file so that I can log the data collected during training. I am already collecting things like the score and reward but I don't know how I could collect things like the policy loss or explained variance where they are changing as the training progresses . I want to log these to an XSLX file every time the reset function is called (once every game) preferably the same one I am logging the score and reward to. The game is the classic snake game, run on pygame. Here is my main code: import gymnasium from stable_baselines3 import A2C from agentStable import snakeEnv from eiffel2 import builder # Import Eiffel2's builder function from torchsummary import summary # from agentStable import data_manager # Initialize your custom environme…  ( 12 min )

Hi, Assume I have data for forex / stocks day trading, where my data/columns are: 1) price of last 50 ticks (a tick is the price at that moment in time, the smallest movement possibly that you can get for that currency) 2) If we should be in a trade (and direction of trade. where 1 = trade going up. 2 = trade going down. 0 = we should not be in a trade). I have tried classification (I generalized the tick price by changing it to pct_change() ) but accuracy is low. would it be possible to reverse engineer through reinforcement learning given these data? I am actually more interested in the trade exiting only (so if trade is currently has value of 1 then it became 0 or 2, it means we should exit existing trade). any guide on how to go about this? Yes I know it will be hard. but if humans can teach a robot to walk, maybe hopefully an agent can be taught to learn to exit a trade based on historucal data? I have done preliminary readings, and is PPO the best way to go? or DQN? assuming I will use stable baseline3. I am also open to using other Python libraries. Thank you. submitted by /u/oniongarlic88 [link] [comments]  ( 9 min )

Hello guys, I made a video for my YT channel discussing some of the greatest success stories in Deep Reinforcement Learning. The video is meant to provide some intuition on RL as a concept as well as a basic understanding of how these different projects work under the hood. There are way too many great RL projects, so I didn’t try to make it an exhaustive list (I’m gonna do more videos later talking about more projects - maybe make a series out of it), but I chose four that I’ve personally worked with in the past/find really insightful and educational (DQN/Atari, Alpha GO, DeepMimic, and Dactyl). Thanks for reading. Here is the link, hope you guys check it out. All feedback is appreciated! https://youtu.be/zOXcNFM8dt4 submitted by /u/AvvYaa [link] [comments]  ( 8 min )

Hi. I'm trying to train a robot that will minic the action that we provide via a video input. On the surface it sounds similar to teaching the robot to walk, but it's not. We can train the robot to make it walk easily these days. But I'm not sure how to teach it to minic an action that we perform. Because each time a new action can be given to the robot and it has to minic that action (it's sort of like a supervised data that the robot has to memorize) Is there a way to do it? is it some branch of machine learning that I'm not aware? The robot is a humanoid simulation. ​ submitted by /u/rakk109 [link] [comments]  ( 9 min )

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I’m having a tough time understanding, how to establish baseline behaviour of agents in a LLM RLHF environment. I have data with time stamp and rewards from several models for each agent. My question is how do we establish baseline behaviour of agents? Does each row in weights and bias considered as a separate agent? Are the initial few 100’s of rewards according to timestamp be considered as baseline behaviour? Thankful in advance. submitted by /u/Private050 [link] [comments]  ( 9 min )

Recently, I've been trying to replicate the method described in the paper "AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control" by training an agent in Isaac Gym using GAIL. However, I've encountered some issues. After adding the discriminator network, the discriminator's loss function stabilizes at around 0.3, and I'm unsure if this value is too high. Additionally, it is strange that the value loss of my value network can reach values between 80 and 90. I want to know if anyone else has experienced a similar situation and what might be the reasons behind these issues. submitted by /u/Mia_Sue_123 [link] [comments]  ( 9 min )

I am following Lil's Weng Blog on RL over here (https://lilianweng.github.io/posts/2018-02-19-rl-overview/) - 1) I am confused how this expression came about - ​ https://preview.redd.it/jo31wbrt2jmb1.png?width=1106&format=png&auto=webp&s=15946bebef2dccadfabf2205d5283729d5405826 2) I am also lost with the origin of this expression - https://preview.redd.it/fs4f46xv2jmb1.png?width=1097&format=png&auto=webp&s=9cc72b265e7f1069bddee9714d1deb7cc3a61775 3) Regarding the second image, where did the expectations go? If you see the top of the image, the state-action value is represented using an expectation but at the bottom, I don't see any expectation. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

New evaluation methods and a commitment to continual improvement are musts if we’re to build multimodal AI systems that advance human goals. Learn about cutting-edge research into the responsible development and use of multimodal AI at Microsoft. The post Frontiers of multimodal learning: A responsible AI approach appeared first on Microsoft Research.  ( 25 min )

Posted by Si-An Chen, Student Researcher, Cloud AI Team, and Chun-Liang Li, Research Scientist, Cloud AI Team Time series forecasting is critical to various real-world applications, from demand forecasting to pandemic spread prediction. In multivariate time series forecasting (forecasting multiple variants at the same time), one can split existing methods into two categories: univariate models and multivariate models. Univariate models focus on inter-series interactions or temporal patterns that encompass trends and seasonal patterns on a time series with a single variable. Examples of such trends and seasonal patterns might be the way mortgage rates increase due to inflation, and how traffic peaks during rush hour. In addition to inter-series patterns, multivariate models process intr…  ( 92 min )

In this post, we build a secure enterprise application using AWS Amplify that invokes an Amazon SageMaker JumpStart foundation model, Amazon SageMaker endpoints, and Amazon OpenSearch Service to explain how to create text-to-text or text-to-image and Retrieval Augmented Generation (RAG). You can use this post as a reference to build secure enterprise applications in the Generative AI domain using AWS services.  ( 7 min )

This post shows you how to configure the Amazon Kendra AEM connector to index your content and search your AEM assets and pages. The connector also ingests the access control list (ACL) information for each document. The ACL information is used to show search results filtered by what a user has access to.  ( 11 min )

Today, we are excited to announce the capability to fine-tune Llama 2 models by Meta using Amazon SageMaker JumpStart. The Llama 2 family of large language models (LLMs) is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Fine-tuned LLMs, called Llama-2-chat, are optimized for dialogue use cases.  ( 46 min )

Recently, generative AI applications have captured widespread attention and imagination. Customers want to deploy generative AI models on GPUs but at the same time are conscious of costs. SageMaker MMEs support GPU instances and is a great option for these types of applications. Today, we are excited to announce TorchServe support for SageMaker MMEs. This new model server support gives you the advantage of all the benefits of MMEs while still using the serving stack that TorchServe customers are most familiar with. In this post, we demonstrate how to host generative AI models, such as Stable Diffusion and Segment Anything Model, on SageMaker MMEs using TorchServe and build a language-guided editing solution that can help artists and content creators develop and iterate their artwork faster.  ( 12 min )

A large class of checksum algorithms have the following pattern: Think of the bits in a file as the coefficients in a polynomial P(x). Divide P(x) by a fixed polynomial Q(x) mod 2 and keep the remainder. Report the remainder as a sequence of bits. In practice there’s a little more to the algorithm than […] Checksum polynomials first appeared on John D. Cook.  ( 6 min )

Before she entered high school, Ge Dong wanted to be a physicist like her mom, a professor at Shanghai Jiao Tong University.  ( 6 min )

Rafi Nizam is an award-winning independent animator, director, character designer and more. He’s developed feature films at Sony Pictures, children’s series and comedies at BBC and global transmedia content at NBCUniversal.  ( 8 min )

Odds are same for each option 1/3. I believe results will be really interesting observation . ​ View Poll submitted by /u/stefanbg92 [link] [comments]  ( 9 min )

Insilico Medicine, an AI-driven biotech company, has announced its AI-designed COVID-19 drug is entering Phase I clinical trials. Promising to deliver lasting results against all variants, this could become the first viable alternative to Paxlovid. To stay on top of such cutting-edge advancements in AI, look here first. Insilico's breakthrough medicine, ISM3312 Generated using Artificial Intelligence, ISM3312 may offer the superior solution to the constraints of current oral medication, Paxlovid. Insilico’s new drug could address the limitations of Paxlovid, including unpleasant side effects and drug resistance due to constant COVID mutation. Preclinical studies reveal the drug’s potential in reducing the viral load in lung tissue and mitigating lung inflammation. Development powered by AI Identified via AI-driven platform PandaOmics, the drug effectively targets crucial proteins in the coronavirus. Using Chemistry42, a generative chemistry platform, Insilico generated new molecules built to suppress this protein, creating ISM3312. Given the success, the company patented ISM3312, which is currently undergoing Phase I Clinical trials, with results expected by end 2023. The Implications Dr. Harvey Castro, an emergency medicine physician, encourages doctors to remain cautious but also recognizes the promise of AI-generated drugs like ISM3312. With the trials in progress, the medical community is closely monitoring it as it could redefine the treatment course for COVID and other similar viruses. Insilico's venture exhibits AI's potential in accelerating effective drug discovery, prompting the need for consistent tracking of AI's transformation of healthcare and other fields. (source) P.S. If you like this kind of analysis, I compile a free newsletter that tracks the most relevant news and research in AI. Professionals from Google, Meta, and Insilico Medicine are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 10 min )

submitted by /u/MarkFulton [link] [comments]  ( 9 min )

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There are tons of TTS software out there, but they don't incorporate human emotions during speech synthesis. For example, anger, tiredness, surprise, happiness... What solutions exist for this today? submitted by /u/ICWiener6666 [link] [comments]  ( 9 min )

Learning a skill, for me, was never about securing knowledge that privileged me over everyone else who did not put the work in. While often, it did feel like drinking Kool-Aid, buying in to these groups like yoga and climbing, I knew I was not there to rub elbows, but to discover the how behind it. Some leaders of some groups did create a barrier of entry, a necessary proving point, but I have always seen these loops to jump through as a challenge - once completed - a spy. Every skill you have learned has prepared you not to be better at that skill, but to learn a new skill with more ease. It is uncomfortable to learn something new, like drinking from a fire hydrant, but the more sips you take from that blasting surge of water, the more you realize it is all part of the process. We get blasted, we sip, we get overwhelmed, we come back. Just because there is a tool that regulates the blasting, that holds our hand through the overwhelm, does not mean all our hard work has been for nothing. In fact, it means we are more prepared, more primed, to receive all of the beauty and knowledge coming our way. Now, friends, we become CURATORS. :) xo submitted by /u/airkaty [link] [comments]  ( 9 min )

submitted by /u/wisconsin-sopapa [link] [comments]  ( 9 min )

Rllib is making me feel like the biggest idiot, again, and maybe someone else knows what I'm doing wrong here? It feels like I'm missing what should be a fairly simple step... I keep receiving the following error message, which is odd, as my environment is an extension of MultiAgentEnv. Is there anything else I need to do in order for my environment to pass the check successfully? ValueError: Have multiple policies , but the env >> is not a subclass of BaseEnv, MultiAgentEnv, ActorHandle, or ExternalMultiAgentEnv! ​ class RoutingEnv(MultiAgentEnv): metadata = { "render_modes": ["human"] } def __init__(self, render_mode="human", **kwargs): super().__init__() ​ ​ gym.envs.register( id="MyEnv-v0", entry_point='routing_rl.envs:RoutingEnv', kwargs={"config": param_config} ) env_name = "MyEnv-v0" train_steps = 200000 learning_rate = 1e-3 save_dir = "saved_models" def register(config): env = gym.make("MyEnv-v0") return env # register the predefined scenario with RLlib register_env("MultiEnv", register) config = ( PPOConfig() .training(lr=0.001, _enable_learner_api=False) .environment(env="MultiEnv") .environment(disable_env_checking=True) .resources(num_cpus_per_worker=1) .rollouts(num_rollout_workers=0) .multi_agent( policies={"shared_policy": PolicySpec()}, policy_mapping_fn=lambda agent_id, episode, worker, **kwargs: "shared_policy", ) ​ submitted by /u/tessherelurkingnow [link] [comments]  ( 9 min )

Hi all, I am training an agent via PPO. The environment is a node removal ('n' number of actions which are nodes on the graph) with evaluation after each node removed. the state is represented by a trained graph attention network in the environment with the average of the node embeddings on the graph representing the state of dimension size 'n'. The embedding of the a node that has been removed is subtracted from overall graph embedding representation to represent the 'removal' of that node. However, I want to absolutely be certain, that given a state representation, in a new unseen graph, the agent will not select a node that is absent from that graph. In the event that the state representation may not be granular enough and might cause the agent to think that a node on the graph is present when it is not, are there ways to mitigate this? Two ideas I have are: mask actions for nodes that are not present (this is already done after node removal to prevent the agent from selecting the same node again), but is this valid to do in an unseen graph if I a priori mask nodes that are not present in the action space Inject a second input to the policy network such as a one-hot encoding of nodes that have already been selected as an input in addition to the state representation of the graph, so that it models finer dependencies between the state and action taken. However is this valid? Any thoughts are appreciated! thank you! submitted by /u/amjass12 [link] [comments]  ( 10 min )

https://zsyncswap.technology/ submitted by /u/shivamrai24 [link] [comments]  ( 9 min )

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As stated here I already emailed the authors and asked in ai.stackexchage, but I haven't received any replies, so I am trying my luck here. I believe the question was clearly stated in the ai.stackexchange link included here again, and the paper in question can be found here. So, I won't repeat it here because the formatting here is worse. I am hoping maybe someone can shed a light on my issue. If this is an inappropriate use of this sub, I'll take the post down :D submitted by /u/carlml [link] [comments]  ( 9 min )

Hi everyone! Last year we launched a tool to make it easier to deploy ML models into production behind REST APIs. Our first prototype was focused on small models built with Scikit-Learn and XGBoost, but pretty quickly we got a lot of requests to support bigger, more complex models built on Tensorflow, Pytorch and Transformers. From detecting model dependencies to building out auto-scaling compute, it's been a lot of fun working through the challenges to make this product scale. We've built a few tutorials to showcase deploying some interesting and complex models to REST Endpoints. The latest one we released is a tutorial showing how to deploy a Grounding DINO model to a Rest API Endpoint for open-set object detection with prompts. Link to blog post tutorial. Link to Colab notebook. https://preview.redd.it/mi3jk4t5jimb1.png?width=950&format=png&auto=webp&s=37524b719f9dd6fb1605d0c18fcec7da31a685dd submitted by /u/Jazzlike_Flamingo_35 [link] [comments]  ( 9 min )

Hi! I am looking for an text classification problem where I can use text data from social media. Similar projects I have found interesting is classifying if the author is depressed, pro-eating disorder, right wing radical, a potential schoolshooter, a bully or a pedophile. If any of you have a suggestion for a classification problem that can be used for something good, please comment. submitted by /u/IndependentSidekick [link] [comments]  ( 9 min )

So I know that embeddings work by finding words that are used in similar contexts or found around some input word. This allows us to find similar words based on proximity to other words and in a way, map a relationship between an input word and other words. But I assume children learn what words mean and the intuition behind them, by hearing the word and associating it with visuals or a specific scenario in front of them which helps them to add context to that word and how it is used. If we were to emulate how children learn words, could we or is there an architecture that allows us to take an input word, find images with the input word in there (Object detection) and then extract the context from the images (other objects and their position and relation to the input word) then convert that context to phrases and query those phrases the next time that a word is inputted to see other phrases or words that are similar to the input word based on whether or not they appear in the images of the input word. Not sure if it makes sense or if it is even useful compared to embeddings but I was thinking about how we could emulate how children learn words to see if we could draw influence from that. Just wondering if there’s a similar approach to this where we use context from images to find similar words and phrases to some input. submitted by /u/4K-AMER [link] [comments]  ( 9 min )

I am currently doing named entity recognition with a bert model. Its working fine so far, so I am now trying to ameliorate my results. Usually my first thought when I try to augment my ML models is input data preprocessing. In case of NER stop word removal and removal of punctuation, numbers and one-character words came to mind - they are hardly ever named entities so I woulndt loose many training examples. However, NER does in fact require context to work, so removing stuff could prove harmfull in the end? I am kind of torn. Should I do it? Are there better data augmentation approaches? I would be really thankfull for any kind of hint submitted by /u/SilverDusk42 [link] [comments]  ( 9 min )

Hello everyone, I know it’s a bit silly to ask these kind of questions, but Im gonna give it a shot since I’ve seen lots of talented people in here. I am gonna try to keep it as short asp.(Also please excuse my "sometimes" bad English, I am not a native speaker) Well, last year I graduated as an industrial engineer, I was thinking during my last year of studies to completely switch to programming since many of my friends are programmers, but they are all web. So I dedicated the last year of my engineering studies to getting to know what machine learning actually is besides my studies (also tbh I wasn’t very consistent) (also my learning material was mostly the famous DL spec by Andrew on coursera), at the end of the year we have something called project of end of studies (like a masters t…  ( 10 min )

Streamlit just launched its latest hackathon focused on large language models and AI 🚀 Awesome opportunity to build a Streamlit app using LangChain, LlamaIndex, AssemblyAI, Weaviate, or Clarifai, and win cool prizes (AirPods, Yeti microphone, mechanical keyboard, to name a few) More info on the hackathon here Streamlit LLM Hackathon submitted by /u/carolinedfrasca [link] [comments]  ( 9 min )

I was just exploring ChatBot and LLMs and found a library named Embedchain AI. This library lets you build a ChatBot like ChatGPT in just 3-4 lines of code. Tutorial: https://www.youtube.com/watch?v=vIhDh7H73Ww submitted by /u/trj_flash75 [link] [comments]  ( 9 min )

“MCMC vs VI” is no longer a discussion about your favourite Roman numeral. If you share my trepidation for model performance in the face of data sparsity, or you simply suffer from anxiety uncertainty, you might be tempted into the Bayesian world. Years later at the precipice of your career (and mental health degeneracy) you over-engineer probabilistic models so intractable that would stress Lord Bayes himself into stomach ulcers. The solution? Approximate inference, the true antihero to model simplification. I wrote a brief primer for those who enjoy maths and those who disdain it, in both cases it's impossible to avoid using maths while discussing Bayesian statistics so I kept it as light as I could. PS - This is a Reddit-friendly copypasta from my medium article, so if you're a visual …  ( 13 min )

I used to do some basic machine learning a few years ago (7+), but then went into what now became data engineering, because of the lack of opportunities in ML. This year I'm trying to up my game and maybe switch back to ML, which I've always been following and tinkering with, but I want to learn all the necessary skills at least at a basic level, in order to find an ML job. I'm learning on my own but now I'm looking for resources regarding MLOps and found ml.school and I'm curious if anyone has any opinons about it or if there is anyone here who has went over the course? Thanks in advance for any help or info! submitted by /u/jack-in-the-sack [link] [comments]  ( 9 min )

Hi I am currently creating computer vision models for segmentation and classification, and I am looking for a tool that is very user friendly. We have been using CVAT so far, and apparently, its UI is too cluttered. So, we need something easier to use. Segment Anything and other auto-segmentation tools simply do not work on our dataset. So, I do not want a tool that is user friendly because it uses AI. Any thoughts? submitted by /u/Avatrin [link] [comments]  ( 9 min )

Hello ML Community, We released our new LLMOps Tool: CometLLM. It's highly optimized for Prompt Engineering Workflows and making it easy to find the best prompts for your use-case! Here a few helpful things you can do with this tool! Score/Rate Your Prompts Add Metadata to your Logged Prompts (Great for Tracking Prompt Usage) Search for Specific Prompts via Keywords/Phrases Visualize Full-On Prompt Chains! Group Your Prompts Hope the ML Community find this useful as well continue to experiment with LLMs! Don't Hesitate to reach out if you have any feedback! submitted by /u/metric_logger [link] [comments]  ( 9 min )

submitted by /u/EducationalCicada [link] [comments]  ( 9 min )

Hello people! This is the first time that I post here and I desperately need your help. I need to perform anomaly detection on a huge network traffic dataset with isolation forest (unsupervised learning). I have the .pcap files of a whole month and and for each day there are multiple devices that communicated each other. So the file of each day is from 700 MB to 2 or 3 GB. My initial idea was to only maintain the header of the packets and to discard the data payload. But even in this case the dataset remains huge and the number of entries is crazy. What I should do? submitted by /u/J-Devesh [link] [comments]  ( 9 min )

"The Greatest Books - Combines many top book lists to create a master list anobii - a community built by readers for readers allowing you to find, shelve, review and share books Author Alcove - Rate read books, shelve to be read, and receive recommendations. BookDigits - Book tracking, rating, and discovery with achievements. Another from an r/books member (I really think this plus authoralcove would be perfect) booklikes - Book tracking and blogging/reviewing Goodreads - The popular choice for book social media, reviews, and tracking LibraryThing - The old standby, of webbased personal library management Litsy - Insagram inspired social media app for tracking and reviwing books Lovelybooks - German book tracking site readernaut - Readernaut helps you make your book list, build a library, keep track of what you've read and what you'd like to read, and then share those lists with your friends. Readgeek - Book review and cataloging site by a redditor(?) and translated from german Riffle - track & reivew books with social media integration TasteDive - (aka tastekid) social rating site for music, movies, shows, books, authors, and games Discovered - Dating site/app for bookworms Calibre - The go to for ebook management The Game of Books - A kickstarter. They used to have a beta up but it's gone now too - http://gameofbooks.com/level_up weread - Encouraging Children To Read: Articles, ideas, and information to encourage children to read thirdscribe - ThirdScribe provides authors and readers with actual tools and services they can use to enjoy their books as well as grow and connect with their audience. What Should I Read Next? - A book recommendation engine bookfinder - book search tool 50 Book Pledge - Goal based book tracking anno.wiki - collaborative book annotation" submitted by /u/Fearless-Room-504 [link] [comments]  ( 10 min )

Hi, I've voiced over 500 videos for a YouTube channel and have the accompanying voiceover audio and scripts. I'd like to train a very robust AI to generate VoiceOver locally and not use an online service using the extensive amount of audio/scripts I have stored. My hardware is a 3070 and 12700. All other solutions have been online such as Elevenlabs. This will be a secondary service I could provide alongside bespoke voice over. submitted by /u/dfawlt [link] [comments]  ( 9 min )

Just wanted to ask what has been lately your experience with availability of GPUs across providers (major ones - AWS, Azure, GCP, but also some minor ones). Especially when it comes to GPUs which are more suited for ML (A100s, H100s). Anyone also considering buying physical hardware instead ? submitted by /u/remek [link] [comments]  ( 9 min )

Soon, I will launch an LLM-powered chatbot. I have run plenty of tests to make sure the LLM works well, but I am super curious about the experience of real (external) users. I’d like to find out if users are happy with the answers the model generates, what topics they ask about, etc. And also how much each user costs me since the service is free and I am paying for it at the moment being. I expect to be able to improve the product over time with this kind of insights. Are you guys trying to track similar metrics? If so, how do you do it? Thank you! submitted by /u/jroux92 [link] [comments]  ( 9 min )

I have been running hyperparameter optimization for an LGBM multi-classifier model with randomized search with 10fold stratified cv as well as oversampling on each fold using SMOTE as follows: # Create a pipeline with SMOTE oversampling smote_pipeline = make_pipeline(SMOTE(random_state=42), lgbm_clf) # Initialize 10-fold stratified cross-validation cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42) # Initialize RandomizedSearchCV for hyperparameter tuning using the pipeline random_search = RandomizedSearchCV( estimator=smote_pipeline, param_distributions=param_dist_with_prefix, n_iter=n_iter, scoring=f1_macro_scorer, n_jobs=n_cores, pre_dispatch=n_cores, cv=cv, random_state=42 I wanted do incorporate early stopping based on the validation set from the nth iteration of cv. However this does not seem possible using the current API if I am correct. If I wanted to use a predefined validation set the code would be sth like this but I want to perform validation using 10 fold-cv validation set only:. mode l= lgb.LGBMClassifier() clf = RandomizedSearchCV( model, parameters, fit_params={ 'early_stopping_rounds':20, 'eval_set':[(X,y)] }, cv=cv ) My questions are: 1- Does it make sense to use early stopping during randomized search? 2- Do you know a way I could do it? 3- If not, is it a good idea to use randomized search without early stopping and train a new model with early stopping using the best parameters resulting from randomized search? Bonus Question: Does it make sense to run randomized search with f1_macro scoring from sklearn instead of multilogloss in case of imbalanced classes? submitted by /u/returnname35 [link] [comments]  ( 9 min )

I'm working my way through various tweaks to a ML pipeline, and I've started keeping short lab notes in a markdown file with just the time, a brief summary of changes, and my observations on training metrics or anything else interesting on a training run. I've also started copying a snapshot of the Python source code to the tensorboard directory, which has saved me a lot of headache. I was wondering how other people keep lab notes, and especially what you find useful to record and how you structure the notes. submitted by /u/hazard02 [link] [comments]  ( 9 min )

Hello, I am currently working on predicting 2D deformations of objects. These objects are available as 2D contours in my code. I am splitting these contours into 1000 points with an equal distance in the direction of the x axis. I have about 70 data entries. The following picture shows one of these objects: Comparison before and after The red data series contains the points before and the blue series contains the points after the deformation. My model should take in a series of coordinates before the deformation. Using this information the model should predict the coordinates after the deformation. I have tried using the LSTM Model from keras. Unfortunately I wasn't able to produce useful results. The way I structured my data is the following: [ [ [x1, y1], [x2, y2], [x3, y3], ... 1000 coordinate pairs ], [ [x1, y1], [x2, y2], ... ], .... 70 entries ] The structure for the input and the output series is the same. When trying to train the model I have a very low loss and low validation loss as well: Overview during training of model The test loss is also quite similar: Overview test loss However when looking into the predictions I get results like these: Visualized prediction after training The prediction is not close to what it should be like. Also the prediction seems to not change even when changing the input. ​ Do you have an idea about why my ML model does not work? Are there examples on this topic available? Should I change my approach in any way? Thank you in advance! Any help is appreciated! ​ If you need my jupyter-notebook, it would be great if somebody could tell me, how to link files on Reddit :) submitted by /u/InitiativeGlass4701 [link] [comments]  ( 10 min )

Hey folks! I wanted to advertise Equinox -- my now-surprisingly-popular ( :D ) JAX library for numerical models. These days that often means "neural networks", but I like to emphasise that this also includes ODEs/SDEs/linear solves, etc. Here's the GitHub link: https://github.com/patrick-kidger/equinox For those already using JAX, then Equinox is interesting because (a) it ships with a NN library, and (b) this is built around the idea that "everything is a pytree", which makes things easy to reason about and easy to compose. Furthermore (c) Equinox offers advanced tools like true runtime errors, out-of-place pytree surgery, and checkpointed while loops, and AFAIK in the JAX ecosystem these are unique to Equinox. For those most familiar with PyTorch: for many use cases (sciML in particular), JAX has a much stronger compiler, more advanced autodiff, etc. And whilst JAX itself is akin to the torch.* namespace, libraries like Equinox are then akin to the torch.nn.* namespace. Because of its speed and features, right now JAX+Equinox is my favourite approach to numerical computing. So I'd love for some more people to try it. What do you think? submitted by /u/patrickkidger [link] [comments]  ( 9 min )

I want to make use of a local rack for running both training and serving jobs. I have looked into using something like Kubeflow, but I have some questions. -Does Kubeflow offer a suitable solution for running tasks across multiple machines? (Either data parallel or model parallel tasks). -How does resource provisioning work with it? Is it able to automatically select the machines that best suits the resource requirements or does it require the user to select where to run the job? Is it able to scale vertically/horizontally? Thanks in advance. submitted by /u/omegalul3000 [link] [comments]  ( 9 min )

Hi r/ML, i want to share a little side project of mine: hydralette. I mainly built this for my own work but thought why not get some feedback and potentially make someone else's work a little easier as well. I think we all agree that having a flexible configuration is crucial to successful ML experimentation. There are a million python config libraries out there, some dedicated to configs like hydra and others that support configs as a convenience feature like transformers.HfArgumentParser. So why did I decide to write yet another library? First off, I can say that I never really liked the way huggingface handles configs. All options are on a single level with tons of dependencies between them, some only taking effect if a combination of others is given. General approach to configs asid…  ( 11 min )

In this post, we introduce a novel method to perform content moderation on image data with multi-modal pre-training and a large language model (LLM). With multi-modal pre-training, we can directly query the image content based on a set of questions of interest and the model will be able to answer these questions. This enables users to chat with the image to confirm if it contains any inappropriate content that violates the organization’s policies. We use the powerful generating capability of LLMs to generate the final decision including safe/unsafe labels and category type. In addition, by designing a prompt, we can make an LLM generate the defined output format, such as JSON format. The designed prompt template allows the LLM to determine if the image violates the moderation policy, identify the category of violation, explain why, and provide the output in a structured JSON format.  ( 13 min )

In this post, we show how the Carrier and AWS teams applied ML to predict faults across large fleets of equipment using a single model. We first highlight how we use AWS Glue for highly parallel data processing. We then discuss how Amazon SageMaker helps us with feature engineering and building a scalable supervised deep learning model.  ( 10 min )

In this post, we target these situations and solve the problem of risking high costs by deploying large foundation models to Amazon SageMaker asynchronous endpoints from Amazon SageMaker JumpStart. This can help cut costs of the architecture, allowing the endpoint to run only when requests are in the queue and for a short time-to-live, while scaling down to zero when no requests are waiting to be serviced. This sounds great for a lot of use cases; however, an endpoint that has scaled down to zero will introduce a cold start time before being able to serve inferences.  ( 10 min )

Out-of-distribution examples are images that are cearly irrelevant to the task at hand. Unfortunately, deep neural networks frequently assign random labels with high confidence to such examples. In this article, I want to discuss an adversarial way of computing high-confidence out-of-distribution examples, so-called distal adversarial examples, and how confidence-calibrated adversarial training handles them. The post Distal Adversarial Examples Against Neural Networks in PyTorch appeared first on David Stutz.  ( 5 min )

Microsoft researchers are proposing a new way to ensure greater trust and accountability in email, texts, direct messages on social platforms, even phone calls, to help mitigate sophisticated threats from AI-related scams and fraud. The post Rethinking trust in direct messages in the AI era appeared first on Microsoft Research.  ( 14 min )

With coral reefs in rapid decline across the globe, researchers from the University of Hawaii at Mānoa have pioneered an AI-based surveying tool that monitors reef health from the sky. Using deep learning models and high-resolution satellite imagery powered by NVIDIA GPUs, the researchers have developed a new method for spotting and tracking coral reef Read article >  ( 6 min )

Creating 3D scans of physical products can be time consuming. Businesses often use traditional methods, like photogrammetry-based apps and scanners, but these can take hours or even days. They also don’t always provide the 3D quality and level of detail needed to make models look realistic in all its applications. Italy-based startup Covision Media is Read article >  ( 7 min )

submitted by /u/Powerful-Pumpkin-938 [link] [comments]  ( 9 min )

Hello, I am learning about natural language processing now. Technically, is this a way for a computer to input language of a person and then convert it into machine code (0s and 1s)? Or, is this a way to turn human language into some computer language like Python, and then turn into machine code as a second step? I am assuming that NLP has only just recently become widely used (like in Chat GPT). Was it a huge jump to go from a machine understanding a computer programming language like Python to a machine understanding ordinary human language? Why was it so much more difficult to train computers to understand the later? Thanks! submitted by /u/NoahsArkJP [link] [comments]  ( 9 min )

Title submitted by /u/ICWiener6666 [link] [comments]  ( 9 min )

Hello all - I'm looking to track down a tool that was able to create a zoom effect that looks three dimensional, example below. https://www.instagram.com/reel/CwLB1XsNK0X/?igshid=MmU2YjMzNjRlOQ== I've searched my usual spots for some different image editing tools and looked at some video ones as well, but I can't quite figure it out. Anyone familiar with a tool that could do something like that? Thanks in advance. submitted by /u/Lys0L [link] [comments]  ( 9 min )

Sounds like the stuff I somgwrite about submitted by /u/Niu_Davinci [link] [comments]  ( 9 min )

Have you ever wondered if AI writing can make you feel better in your head? I would like to discuss about how AI writing can put a smile on your face as it is my personal experience. 1. Stress-Free Writing Writing can be stressful, especially when you're not sure where to start. AI writing tools can be your stress-busters. They help you begin by giving you ideas and suggestions. So, no more staring at a blank screen in frustration! 2. Beating the Writer's Blues We all know that feeling when words just won't flow. AI can be your brainstorm buddy. It tosses out ideas like confetti at a party, sparking your creativity when you need it most. Goodbye, writer's block! 3. Making Your Writing Shine Typos and messy sentences can be a downer. AI can be your proofreader, catching those pesky er…  ( 10 min )

Amazon India is developing a generative artificial intelligence (AI) tool called SahAI (help/assist) for its business partners to help them with the backend of any particular product.[1] A robot moves a toy package of butter around a table in the Intelligent Robotics and Vision Lab at The University of Texas at Dallas. With every push, the robot is learning to recognize the object through a new system developed by a team of UT Dallas computer scientists.[2] “What is my purpose?” – “You pass butter”. Mustafa Suleyman, Google DeepMind’s co-founder and chief executive of Inflection AI, told the Financial Times that the US should use their chip leadership to enforce minimum global standards for the use of AI.[3] Model who never ages: Noonoouri becomes first digital artist to be signed by Warner Music.[4] Sources: [1] https://www.thehindu.com/sci-tech/technology/amazon-working-on-a-generative-ai-to-help-small-businesses-in-india/article67255325.ece [2] https://www.nanowerk.com/news2/robotics/newsid=63572.php [3] https://www.finextra.com/newsarticle/42878/google-deepmind-co-founder-argues-us-should-set-ai-global-standards---ft [4] https://www.thenationalnews.com/arts-culture/music-stage/2023/09/02/model-who-never-ages-noonoouri-becomes-first-digital-artist-to-be-signed-by-warner-music/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

This story, it's narrator and even the cover art are all made by Artificial Intelligence. The only human contribution was adding the Text to the book cover and the prompts used to produce the story. https://youtu.be/tZgq9N9RCo0 submitted by /u/BermaidMutter [link] [comments]  ( 9 min )

Scammers are now using AI to create realistic voice deepfakes, aiming to trick people into transferring money. By mimicking real customer voices, this new type of voice fraud attempts to exploit bank security systems and deceive call center agents. To make sure you're updated about the latest AI trends, look here first. Increasing prevalence and sophistication of voice frauds A rise in AI-generated voice frauds has been noted this year, with one major case featuring an investor in Florida whose voice was synthetically duplicated to deceive his bank. Voice authentication vendor Nuance detected its first successful deepfake attack on a financial services client late last year. These scams are facilitated by the wide availability of voice samples online, coupled with the growth of AI capabilities and hackers' access to stolen bank account details. Defending against evolving AI threats Currently, only a small percentage of fraud calls to large financial companies are AI-generated. Most attacks have targeted credit card service call centers. Fraudsters are advancing their techniques, now able to convert speech to a specific target's voice in real-time using advanced AI systems like Microsoft's VALL-E. With most of these security measures focusing on call centers and automated systems, individual calls to high-ranking officials remain a vulnerability. (source) P.S. If you like this kind of analysis, I write a free newsletter that keeps you updated with the most relevant news and research in AI. Join professionals from Google, Meta, and OpenAI who are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

I came across a paper by Andrew Ng "https://ai.stanford.edu/~hllee/icml07-selftaughtlearning.pdf" ，with title " Self-taught Learning: Transfer Learning from Unlabeled Data " I am not an expert on this topic, but I feel it is really close to what SimCLR or MoCO are trying to do. Can someone provide guidance on what different it is between self-taught learning and self-supervised learning? submitted by /u/AaronSpalding [link] [comments]  ( 9 min )

Paper: https://www.science.org/doi/10.1126/sciadv.adi2947#abstract Code: https://zenodo.org/record/8037309 Abstract: Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation. https://preview.redd.it/5lcx3sn8ramb1.jpg?width=711&format=pjpg&auto=webp&s=4431b81708bb9ab98e6351f4b979897ad8244ed9 https://preview.redd.it/vgsuqsn8ramb1.jpg?width=718&format=pjpg&auto=webp&s=f0602185fcb0dc6ec29308f77f1db77a4f4a562d https://preview.redd.it/hpfuftn8ramb1.jpg?width=709&format=pjpg&auto=webp&s=545f4fab3033cb68637052e7ff2c4775a12a7b99 https://preview.redd.it/7plm0tn8ramb1.jpg?width=714&format=pjpg&auto=webp&s=b138b1c43a2078297b69c09d26de013865629e77 https://preview.redd.it/uc6tnrn8ramb1.jpg?width=703&format=pjpg&auto=webp&s=fcf747b2515fbf6e78b1ef7aa66ce9ca4d223cd3 submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Many natural language processing tasks benefit from long inputs, but processing long documents with Transformers is expensive -- not only due to quadratic attention complexity but also from applying feedforward and projection layers to every token. However, not all tokens are equally important, especially for longer documents. CoLT5, a long-input Transformer model that builds on this intuition by employing conditional computation, devoting more resources to important tokens in both feedforward and attention layers. CoLT5 can effectively and tractably make use of extremely long inputs, showing strong gains up to 64k input length. In this video, we walk through the ColT5 paper and explain what is T5, longT5, UL2 and PEGASUS, then discuss how ColT5 has advantage over previous methods for few-shot and 1-shot tasks. https://youtu.be/8KCQQtXje2g?si=ecbvnFPlhGP01aOt submitted by /u/MRMohebian [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.00071 Github: https://github.com/jquesnelle/yarn Very informative Reddit discussion: https://www.reddit.com/r/LocalLLaMA/comments/166jik4/128k_context_llama_2_finetunes_using_yarn/?utm_source=share&utm_medium=web2x&context=3 Twitter: https://twitter.com/EnricoShippole/status/1697317625116742119?s=20 Abstract: Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models. However, these models fail to generalize past the sequence length they were trained on. We present YaRN (Yet another RoPE extensioN method), a compute-efficient method to extend the context window of such models, requiring 10x less tokens and 2.5x less training steps than previous methods. Using YaRN, we show that LLaMA models can effectively utilize and extrapolate to context lengths much longer than their original pre-training would allow, while also surpassing previous the state-of-the-art at context window extension. In addition, we demonstrate that YaRN exhibits the capability to extrapolate beyond the limited context of a fine-tuning dataset. We publish the checkpoints of Llama 2 7B/13B fine-tuned using YaRN with 64k and 128k context windows at https://github.com/jquesnelle/yarn . https://preview.redd.it/tnovsbpjiamb1.jpg?width=1354&format=pjpg&auto=webp&s=ce098b3071285f9f64d99312a98999de8b625bfe https://preview.redd.it/j10sicpjiamb1.jpg?width=997&format=pjpg&auto=webp&s=95bbc6d70759ef7ccdf6bccee0c2a2f98ebda52b https://preview.redd.it/ve710dpjiamb1.jpg?width=1380&format=pjpg&auto=webp&s=05f53117bcf648e330fa6ac148746484dca9fb1b ​ ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Overcoming present challenges in text-to-image models, DenseDiffusion is the latest advancement ensuring enhanced image quality based on scene descriptions. Developed specifically to handle complex captions, it brings a new era in dense captioning. https://preview.redd.it/v5oa5suwfamb1.png?width=2000&format=png&auto=webp&s=17fbcc702ee21a41cb356a7d0e38d710a8c048c3 If you want to stay on top of the latest trends and insights in AI, look here first. Why is it noteworthy? It addresses the issues with existing techniques where users face inconsistencies when dictating the arrangement of elements within generated images using textual prompts. DenseDiffusion is training-free, unlike existing methods like "Make-aScene" and "Latent Diffusion Models," which are computationally intensive and r…  ( 9 min )

Let's say your training a simple CNN for a classification problem. An example would be a model that is supposed to decide if a person is male or female based on facial images. What is your experience regarding image sequences from videos in the training datasets? My intuition is, that the added information to the dataset from one video isn't proportional to the number of frames. The network probably can't learn much more from 30 frames with little variation in comparison to a single image (at least if you use augmentations). What do you think about this? Or do you even know any research in the direction of this question? submitted by /u/seba07 [link] [comments]  ( 9 min )

Hey all, I'm a screenwriter that's curious about ML/AI tech and its applications to my industry. I'm wondering what the current best product is for writers and editors. Specifically, I'm curious if there's a product that can "edit" longform text - say, to trim a screenplay down from 140 pages to 120, while retaining style, plot, and narrative intent. Are there any products like that? Forgive me if this is too basic; I've only dabbled in ChatGPT and MidJourney to see what the fuss is about. Thanks in advance! submitted by /u/cesrep [link] [comments]  ( 9 min )

There is so much going on right now in AI and machine learning. But there isn't a concise place to find experts, teams, and amazing projects all in one place. That is why we are building Bazaar, the first ever LLM marketplace. We will be inviting slowly making sure we have enough members on each side of the marketplace. https://www.llmbazaar.com/ submitted by /u/husky_misconception [link] [comments]  ( 9 min )

I'm writing a neural network for super resolution but it's one of my first projects and I didn't really understand what compile() is used for. I specify the optimizer, the loss and the accuracy metrics in the trainer class and then I just call my train method on my model. Should I still use the compile function? I'm following this template for the project structure https://github.com/jinh0park/Tensorflow-2.0-Project-Template/tree/master submitted by /u/petrogass [link] [comments]  ( 9 min )

Project page: https://pmh9960.github.io/research/GCDP/ https://i.redd.it/9uz2wt3ba9mb1.gif submitted by /u/yeolj0o [link] [comments]  ( 9 min )

My question is more focused on the pre-processing side, rather than the training side of things. I have a local RAG Q&A pipeline set up for personal documents (local regulations, technical manuals, stuff like that), and I'm looking for ways to improve it. All the documents I'm working with are consistently structured, with nested bullets of varying depth making up most of the structure. So far I've been manually writing/tweaking a python script I wrote to recursively extract the nested bullets and duplicate their hierarchy parents' content for each of the inner-most bullets, that way each bullet has all the contextual content it needs to be valuable in a vacuum. So something like: (a) 1. A. B. Would turn into: (a) + 1. + A. (a) + 1. + B. This works well in the sense that my LLM does a wonderful job answering my questions and citing the right sources, but the lion's share of my work goes into the tweaking of my parser scripts, or creating new ones entirely. I've played around with semantic segmentation via embedding models, but it doesn't really work here since I'm trying to retain the nested structure of the document for citation accuracy. Does anyone have any ideas for ots solutions that address this kind of thing? I can't be the only person who has run into this type of problem, but I've been having a really hard time finding relevant libraries/software that can even get me 80% of the way there. Also, I'm totally happy to hear what you guys have done and how's it's worked out/what walls you've hit! Edit: I suppose I should have included my current attempt as well, so it's doesn't look like I'm treating this subreddit like Google lol https://gist.github.com/apettina/76de292d6d24ed3d0128b87847706b18 submitted by /u/RedditAppSucksDicks [link] [comments]  ( 10 min )

Hey folks, it's me the dlt builder again. I have questions about data contracts! Schema evolution, where the schema of the destination evolves based on incoming data is nice for ingesting transactional data. However, there are scenarios where we might not want this automatic evolution. For example, when other parts of our infrastructure require a fixed schema or when we want to store only data that conforms to the current schema. This is where a data contract comes into play. Our plan is to implement a straightforward version of this concept initially. We're considering introducing settings on the pipeline to control schema evolution, and here are some modes we're thinking about: Evolve (Default): The current behavior where the schema adapts to incoming data. Freeze-and-Trim: Freez…  ( 10 min )

Classy-fire is a pretrained multiclass text classification approach that leverages Azure OpenAI's LLM APIs using clever parameter tuning and prompting for classification. Why? Tired of having to beg your LLM to pick from a set of options / actions? Tired of working hard on cleaning and parsing its responses to trigger a flow? Struggling to strip unhelpful prefixes (such as "Sure! " or "I am just a language model!")? Having to wait on retries in cases of unexpected outputs? Getting random responses on the same query? Need a "quick and dirty" text classifier? Don't have enough training data? submitted by /u/shayben [link] [comments]  ( 9 min )

I've been an ML/software engineer for a bit over 7 years now, and am looking for a new job. It seems like most of the job postings I see around want experience with large compute clusters, but my work has always been in compute-restricted domains (robotics, on-prem deployments, etc.). I'm looking broaden my skillset and get some experience with distributed computing. Does anyone know of open-source or otherwise public projects that work with compute clusters like this that are looking for volunteers? I'm happy to put aside an hour or so a day to work on an interesting project. submitted by /u/Flag_Red [link] [comments]  ( 9 min )

https://psyarxiv.com/4cbuv/ The short of the paper is they show that an AI algorithm that can only learn via sampling from human action is unable to tractably simulate human behavior. I have seen papers like this one by u/alcanthro questioning the validity of the result, but I want to point out two objections to the paper that stand even if the result is true. ​ 1 - It only seems to apply for AIs trained to mimic humans via sampling human behavior: The paper assumes the AI is trained via an arbitrary machine learning algo M that samples from possible human behaviors in given situations. This matches pretty well to how a lot of LLMs are pretrained (guess the next token), but doesn't seem to apply to any sort of reinforcement learning, since in those situations you are not training the …  ( 11 min )

This was a small project I was working upon which adds a recurrent prior to attention-based models. This allows integrating an adaptive-computation mechanism, leading to much better length-extrapolation capabilities (compared to vanilla transformers). On some tasks, I'm able to OOD extrapolate to quite an appreciable extent! Its also (relatively) quite parallelizable with slightly different training regimes - thus, hopefully being scalable as well. Being lightweight, it might be useful for inferencing as it saves on memory (trading off compute instead). It's interesting to think that MHSA might contain an implicit inductive bias that prevents extrapolation. Replacing that with other variants helps a lot - I go in detail in the writeup! Twitter summary: https://twitter.com/awesome_ruler_/status/1698668965612917112?s=20 Writeup/Blogpost: https://dripfeedofideas.notion.site/dripfeedofideas/ReAct-bef052956a0d45f29fb5a5383e7d737d GitHub repo: https://github.com/neel04/ReAct submitted by /u/Competitive-Rub-1958 [link] [comments]  ( 9 min )

A while back, the IB principle (https://arxiv.org/abs/1503.02406) made a few waves as a promising framework to understand/study deep neural networks. But I recall a series of follow up works (notably https://openreview.net/forum?id=ry_WPG-A-) that called a lot of the results into question, and (I think?) people drifted away from it. I saw this recent paper (https://arxiv.org/abs/2304.09355) on the IB and self-supervised learning, and it got me wondering what the current views are as to how useful/accurate the IB view of deep learning is? submitted by /u/Tea_Pearce [link] [comments]  ( 9 min )

I was looking over this paper https://arxiv.org/abs/2110.01691 called AIChains that deals with an interactive chaining method to interact with LLMs. I could not find an associated codebase with that paper. If I wanted to create a similar UI like theirs anything you would recommend? More specifically, if I want to replicate the paper in 3 months full time (as a student with some experience in ML), what would be the best approach to the UI part of the paper. What if deployment is a concern? I was intially thinking of simple python frameworks like PySimpleGui, or maybe something more comprehensive like PyQt. I am rather unfamiliar with more common web frontend frameworks, but if there are suggestions that make such a Graph/Diagram based User interface easy to implement, I am open to them. submitted by /u/BasisCompetitive6275 [link] [comments]  ( 9 min )

There have many blogs and papers disscuss SDE for diffusion model: Stochastic Differential Equations and Diffusion Models https://www.vanillabug.com/posts/sde/ Perspectives on diffusion https://sander.ai/2023/07/20/perspectives.html On the Mathematics of Diffusion Models https://arxiv.org/abs/2301.11108 But i can't find blog or book to explain Stochastic Differential Equations, it seems complex, even after i have learned Calculus and Ordinary Differential Equations and Partial Differential Equations, i still can't understand SDE, especially the SDE Perspective on diffusion. So Do you know some blogs or books explain SDE intuitive like betterexplained.com/ and mathsisfun.com/ ? submitted by /u/ghosthamlet [link] [comments]  ( 9 min )

How MoEs (sparsely gated ones) are trained appear to be rather opaque from looking at literature (e.g. GLAM and similar papers). From my intuition it would make sense that it works either by: Each expert being trained on a subset of data (the data they are supposed to have expertise in) to predict a token given a previous token, or to predict a token given a contextual embedding. This would mean the expert MLPs are frozen, and the only thing concerning the experts that we train with the transformer is the gating mechanism. or The experts are trained in the same training loop as the transformer (e.g. backprop over the whole network), but that each of the experts are only trained on a subset of the data corresponding to their expertise (e.g. as we perform the training loop and we run upon data from our math dataset, then we backprop through the math expert mlp) ​ Could anyone help me resolve my confusion and point me in the right direction for how these are trained? Thanks! submitted by /u/SorasNobody [link] [comments]  ( 9 min )

I'm from India and I've started learning and building my portfolio in Machine Learning/Deep learning. Currently, I'm doing "Practical Deep learning using fast.ai and pytorch" course. In my university, there are not a lot of companies visiting for campus placement this year so I decided to go on the offcampus job hunt. I'm a final year student (in masters but bachelors was unrelated to CS) and no work experience. I have further personal goals for which I'd need a steady and good income. I decided if I could get a remote job it would be really beneficial for me as my living costs would be saved and I'll be paid much more than what India offers freshers(since I'll be paid in dollars or euros). However, I need advice in various domains: 1. Should I focus on one of ML/DL/ Data science or multiple ? 2. Any resources that could help me learn ? 3. Projects that help me stand out from the crowd? 4. Where can I start looking for remote work(websites, etc)? 5. Any other personal advice is appreciated! Thank you for taking the time to read my post :) submitted by /u/Lazy_Guidance_5151 [link] [comments]  ( 9 min )

Hello everyone, In deep learning finetuning pre-trained model was performed by taking some pretrained models like resnet, vgg and unfreezing some of it's final layers. Is it the same when finetuning pretrained ViT models? Or do we have to take pretrained ViT and train all the parameters on our own data ? On this tutorials https://theaisummer.com/hugging-face-vit/, they have not freezed any pretrained layers. submitted by /u/Bishwa12 [link] [comments]  ( 9 min )

So apparently NNs may not outperform simple greedy algorithms in some combinatorial optimization problems Never thought could be the case. https://arxiv.org/pdf/2206.13211.pdf… Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like Maximum Independent Set. https://arxiv.org/pdf/2210.00623.pdf… Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-Cut submitted by /u/vniversvs_ [link] [comments]  ( 9 min )

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Hi all, I have just completed my MSc and am looking for open PhD positions (preferably funded) in RL to join. My masters thesis was on Hierarchical RL and skill discovery, so that’s the domain am mostly interested in since I have spent quite some time researching it but also open to other interesting avenues. If there are any such positions available at your workplace/lab please let me know. Thanks….. submitted by /u/FreakedoutNeurotic98 [link] [comments]  ( 9 min )

I am reading Sutton's RL chapter on Policy Gradients (13.1) and came across the following paragraph. Can someone please explain it to me - " Finally, we note that the choice of policy parameterization is sometimes a good way of injecting prior knowledge about the desired form of the policy into the reinforcement learning system. This is often the most important reason for using a policy-based learning method. ". Is he referring to some kind of Bayesian technique? I'd highly appreciate some examples here. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Hey, I've been going through the papers curated by people behind OpenAI Spinning Up and I've recently started thinking what the list would look like in 2023 if OpenAI hadn't abandoned it. Do you folks have any suggestions for DRL papers from 2019, 2020, …, up to now? submitted by /u/spoiled-mylk [link] [comments]  ( 9 min )

i want to create an ai for Rivals of Aether to see how far it could get in abyss mode and if it could beat 3 9th level cpus on a team. i have no idea how to do this. I was thiking for abyss mode, it could get rewards for finishing waves, and get more reward for doing them with minimal damage. ​ submitted by /u/Additional_Ad9093 [link] [comments]  ( 9 min )

Given a square complex matrix A, the Jordan normal form of A is a matrix J such that and J has a particular form. The eigenvalues of A are along the diagonal of J, and the elements above the diagonal are 0s or 1s. There’s a particular pattern to the 1s, giving the matrix J […] Jordan normal form: 1’s above or below diagonal? first appeared on John D. Cook.  ( 6 min )

When is the discrete Fourier transform of a vector proportional to the original vector? And when that happens, what is the proportionality constant? In more formal language, what can we say about the eigenvectors and eigenvalues of the DFT matrix? Setup I mentioned in the previous post that Mathematica’s default convention for defining the DFT […] Eigenvectors of the DFT matrix first appeared on John D. Cook.  ( 6 min )

Underscoring NVIDIA’s growing relationship with the global technology superpower, Indian Prime Minister Narendra Modi met with NVIDIA founder and CEO Jensen Huang Monday evening. The meeting at 7 Lok Kalyan Marg — as the Prime Minister’s official residence in New Delhi is known — comes as Modi prepares to host a gathering of leaders from Read article >  ( 5 min )

paper https://arxiv.org/abs/2308.09853 abstract. This paper investigates the rational thinking capability of Large Language Models (LLMs) in multi-round argumentative debates by exploring the impact of fallacious arguments on their logical reasoning performance. More specifically, we present Logic Competence Measurement Benchmark (LOGICOM), a diagnostic benchmark to assess the robustness of LLMs against logical fallacies. LOGICOM involves two agents: a persuader and a debater engaging in a multi-round debate on a controversial topic, where the persuader tries to convince the debater of the correctness of its claim. First, LOGICOM assesses the potential of LLMs to change their opinions through reasoning. Then, it evaluates the debater’s performance in logical reasoning by contrasting the scenario where the persuader employs logical fallacies against one where logical reasoning is used. We use this benchmark to evaluate the performance of GPT-3.5 and GPT-4 using a dataset containing controversial topics, claims, and reasons supporting them. Our findings indicate that both GPT-3.5 and GPT-4 can adjust their opinion through reasoning. However, when presented with logical fallacies, GPT-3.5 and GPT-4 are erroneously convinced 41% and 69% more often, respectively, compared to when logical reasoning is used. Finally, we introduce a new dataset containing over 5k pairs of logical vs. fallacious arguments. The source code and dataset of this work are made publicly available. GPT3.5 vulnerable to false information generated by itself! submitted by /u/Amir-AI [link] [comments]  ( 9 min )

I'm asking because it seems like when I review other people's work I very regularly catch a tiny coding misstep that has HUGE downstream implications. I'm sure my own work is not exempt either. Some examples: "At this step you're saying you encode responders as 1 and non-responders as 0 but you actually did it the other way around." "That groupby statement isn't doing what you think it's doing." "When you created your target variable by labeling people with this ratio >= 30%, you accidentally failed to capture a ton of actual responders, because the floating-point arithmetic used to derive this column is calculating people with actual values of 0.30 as 0.2999999999999998." Come on guys, let's hear it. submitted by /u/WartimeHotTot [link] [comments]  ( 9 min )

Meta has recently unveiled DINOv2, its cutting-edge computer vision model, and FACET, a comprehensive benchmark to ensure AI fairness. These developments promise improved automation and better inclusivity in the AI sector. If you want to stay on top of the latest trends and insights in AI, look here first. https://i.redd.it/jeojm1qew3mb1.gif DINOv2 for advanced visual tasks Meta has made the powerful DINOv2 model available under the Apache 2.0 license, employing self-supervised learning to enhance image segmentation and depth estimation. This broader use model encourages further innovation and practical application in the computer vision community, driving progress in the AI industry. FACET for enhanced AI fairness Given the inherent difficulty and risks in ensuring fairness in computer vision, Meta introduced FACET. FACET has been developed to benchmark fairness across computer vision models performing tasks such as detection or classification, considering a wide array of demographic attributes. This revolutionary tool enables a better understanding of potential biases in AI models, helping to address fairness and robustness concerns. Wider implications Preliminary studies indicate performance disparities across some demographic groups within computer vision models. FACET allows researchers to track these divergences and monitor the implementation of corrective measures. Meta actively encourages researchers to use FACET for fairness benchmarking in other visual/multimodal tasks. For instance, the DINOv2 model's performance was analyzed with FACET — facilitating insights into potential biases. (source) P.S. If you like such analysis, I write a free newsletter tracking significant news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Introduction to the problem I will provide you with link to github code so you can see both input dataset, how my onehot encoding and decoding works and implementation of VAE, with current reconstruction_loss and kl_loss fuctions that make my loss function. dataset - amp.csv onehotencoding -- tools.py main code -- VAE-onehot-testing.ipynb https://github.com/aronta/Master-thesis-Generating-de-novo-peptides-using-variational-autoencoder-model/blob/main/VAE-onehot-testing.ipynb Firstly, just to explain dataset. I have sequences of peptides represented with onehot encoded sequences which I am using as input for my model. Current VAE model, both encoder and deocder are based on LSTM layer as a main way for this VAE to learn connections between inputed sequences and to make sense of it all. Main Issue Issue is that the latent space im getting doesn't look good no matter what i do. (pictures of plots are on link). So i have tried scalling kl loss (and also warming it up -- because many papers say its a good way) but it doesn't change the end result. Maybe there is problem in the implementation of the VAE, i am realy not sure. The main goal would be (like in all VAE implementations) generating new sequences from latent spaces that make sense, opposed to the current outputs that im getting. My guess There is a problem with optimizing loss function, but i could be completely wrong (maybe the model is wrong for the input I have, or onehotencoding isn't even a good way to represent data entering LSTM layer). submitted by /u/Yupgrade [link] [comments]  ( 9 min )

I've been learning MERN with a course (charging INR 6k) for last 3 weeks, won two hackathons and Contributed to OS projects. Now just 1 week ago I saw a remote ML job Profile that required OS Contribution to apply.I looked through their docs, learnt python, tensor flow basics and I Contributed to their Tensor flow and Paddle module and got 3 - 4 PR merged(Enough to apply) . Now I'm confused what to continue with, should I do both or do it one by One. I'm a recent graduate so need a job ASAP but I can give maximum time of the day to study. Please can someone give some advice so I can make my decision, as I'm unable to leave either TLDR; Learnt both ML and MERN, Contributed and now confused what to carry on with as I need a job asap. submitted by /u/Sinofdracry [link] [comments]  ( 9 min )

Hi guys, I'm trying to compute the percentage of each language appearing in a document. My current use cases including two known languages and a set of documents which have the two languages mixed in (code switching, due to translation error). I'm training an ML model to make the output monolingual (leaned towards a designated language), so I need a reliable measure to estimate whether the ML model is making progress or not. Currently, I use lingua with the `compute_language_confidence_values()` function but the prediction is quite poor. For example, given a piece of text in Japanese and English: from lingua import Language, LanguageDetectorBuilder languages = [Language.ENGLISH, Language.JAPANESE] detector = LanguageDetectorBuilder.from_languages(*languages).build() detector.compute_language_confidence_values("わかりません hey do you understand me hey oh really") >>> [ConfidenceValue(language=Language.ENGLISH, value=1.0), ConfidenceValue(language=Language.JAPANESE, value=0.0)] So it's not quite correct (should be 0.8-0.2 or something similar), does anyone have any advice ? Or are there better softwares out there ? submitted by /u/KarmaCut132 [link] [comments]  ( 9 min )

Does anyone have any literature on how to constrain the output of an LLM to a specified format? I’ve self hacked a method to get LLAMA to output a json of perfect schema. I tried to find something out of the box but I couldn’t find anything, and so I home brewed it. Thinking of publishing a paper on this but I don’t want to republish something already written, so asking here first. Thanks! submitted by /u/SnooPears7079 [link] [comments]  ( 9 min )

Does anyone have any literature on how to constrain the output of an LLM to a specified format? I’ve self hacked a method to get LLAMA to output a json of perfect schema. I tried to find something out of the box but I couldn’t find anything, and so I home brewed it. Thinking of publishing a paper on this but I don’t want to republish something already written, so asking here first. Thanks! submitted by /u/SnooPears7079 [link] [comments]  ( 9 min )

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My Code (Colab Link) Hi! For the past few days, I've been trying to fine-tune a model using TPU parallelism / FSDP with a Kaggle TPU notebook. The reason I need to set up FSDP is because the model I'm using is very large (Openlm's open llama 3b v2). When I try to fine-tune it, I quickly run out of memory on the TPU. Linked above is my code, if anyone has any useful information I would greatly appreciate it! Thank you!! Edit: Also providing my code through text here: !pip install sentencepiece !pip install -U accelerate !pip install -U transformers !pip install cloud-tpu-client !pip install torch-xla !pip install pyarrow import torch import torch_xla import torch_xla.core.xla_model as xm from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments import pand…  ( 10 min )

Can someone list down one dataset for Algo Trading Simulation or free API endpoint , and i will go over following Algorithms: Basic KNN (moving average) SVR other ML models if any LSTM other DL models if any any RNN model basically my mission is to write a paper at then end of months comparing all algorithms with candle stick patterns over different strategies ​ submitted by /u/reactwebdev [link] [comments]  ( 9 min )

Just curious to know the thoughts of other Masters/PhD students, professors or others in academia or industry research about their experience with regard to the title. submitted by /u/V1bicycle [link] [comments]  ( 9 min )

I'm the author of TokenMonster, a free open-source tokenizer and vocabulary builder. I've posted on here a few times as the project has evolved, and each time I'm asked "have you tested it on a language model?". Well here it is. I spent $8,000 from my own pocket, and 2 months, pretraining from scratch, finetuning and evaluating 16 language models. 12 small sized models of 91 - 124M parameters, and 4 medium sized models of 354M parameters. Here is the link to the full analysis. Summary of Findings Comparable (50256-strict-nocapcode) TokenMonster vocabularies perform better than both GPT-2 Tokenizer and tiktoken p50k_base on all metrics. Optimal vocabulary size is 32,000. Simpler vocabularies converge faster but do not necessarily produce better results when converged. Higher compre…  ( 10 min )

I'm a machine learning engineer and researcher. I got fed up with how difficult it is to understand why neural networks behave the way they do, so i wrote a library to help with it. Comgra (computation graph analysis) is a library you can use with pytorch to extract all the tensor data you care about and visualize it graphically in a browser. This allows for a much more detailed analysis of what is happening than the usual approach of using tensorboard. You can go investigate tensors as training proceeds, drill down into individual neurons, inspect single data sets that are of special interest to you, track gradients, compare statistics between different training runs, and more. This tool has saved me a ton of time in my research by letting me check my hypotheses much more quickly than normal and by helping me understand how the different parts of my network really interact. I hope this tool can save other people just as much time as it did me. I'm also open for suggestions on how to improve it further: Since I'm already gathering and visualizing a lot of network information, adding more automated analysis would not be much extra work. submitted by /u/Smart-Emu5581 [link] [comments]  ( 9 min )

https://youtu.be/V4dO2pyYGgs ReST uses a bootsrap-like method to produce its own extended dataset and trains on ever higher-quality subsets of it to improve its own reward. The method allows for re-using the same generated data multiple times and thus has an efficiency advantage with respect to Online RL techniques like PPO. ​ Paper: https://arxiv.org/abs/2308.08998 ​ Abstract: Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences. We propose a simple algorithm for aligning LLMs with human preferences inspired by growing batch reinforcement learning (RL), which we call Reinforced Self-Training (ReST). Given an initial LLM policy, ReST produces a dataset by generating samples from the policy, which are then used to improve the LLM policy using offline RL algorithms. ReST is more efficient than typical online RLHF methods because the training dataset is produced offline, which allows data reuse. While ReST is a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that ReST can substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner. ​ Authors: Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat, Nando de Freitas submitted by /u/ykilcher [link] [comments]  ( 9 min )

I had been exploring convergence properties of MAML and there are some recent works establishing convergence under certain conditions. I am trying to understand how this would play out with GAN's, I know that in general training is generally unstable and there are a lot of issues such as memorization and mode collapse under this regime, but I am looking for a theoretical result, for instance we know that GAN's converge under ideal conditions and we also know that MAML converges, can we make any comments on the convergence properties of GAN's when trained using MAML, ideally a neat trick to know if they will converge based on what we already know? The proof for MAML convergence is fairly complicated and I expect that a proof that has additional second order gradient terms and feedback loops will probably involve a lot of work and I am wondering if anyone could provide some sort of insight or intuition as to what such a result would look like? Thanks submitted by /u/ashblue21 [link] [comments]  ( 9 min )

Problem: Given time series data of the last few years, one data point per day (eg price of a product or sales made this day). My job is to predict the next 7 days, ie. 7 scalars. Approach: Train one model for each time lag. The first model predicts tomorrow, the second model the day after tomorrow and so on (7 models in total). The features are the last prices of the last 7 days and some saisonal features (calendar week, price on this day last year and so on). Question: is there anything wrong with this approach? It doesn’t feel like the most elegant method to train 7 separate models. The problem with using a single model is, that this model must be able to predict 7 values of different points in time (and i don’t want to give the model input data of 7 days and let it predict all 7 scalars at once. The model should only use the features of a single day to predict this day). The 2 other to options I have considered are to train an autoregressive model (model just learns to predict the next day. To predict the day after tomorrow you give it its own prediction as input). Or to build a „time-lag“ feature, which tells the model how far in the future this datapoint lies. But this doesn’t make sense, because there is nothing like a weekly trend or so. What do you think? The autoregressive approach is elegant, but its implementation and maintenance is complex. submitted by /u/Individual-Cause-616 [link] [comments]  ( 9 min )

I've written a post on how to use stable diffusion for data augmentation for object detection and segmentation. Please check it out and share some insights on how to evaluate these kind of tasks. https://medium.com/@kaushik.koneripalli/satellite-image-data-augmentation-using-stable-diffusion-for-object-detection-segmentation-8b1fe87b969 submitted by /u/perceptron333 [link] [comments]  ( 9 min )

I created a open-source star removal tool "star2k13". Would love to hear some feedback . Here is link to the tool : Starrem2k13: Open source star removal tool (code2k13.github.io) Works on most operating systems and docker submitted by /u/Key_Education_2557 [link] [comments]  ( 9 min )

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Meta has recently unveiled DINOv2, its cutting-edge computer vision model, and FACET, a comprehensive benchmark to ensure AI fairness. These developments promise improved automation and better inclusivity in the AI sector. If you want to stay on top of the latest trends and insights in AI, look here first. https://i.redd.it/zg47br3xv3mb1.gif DINOv2 for advanced visual tasks Meta has made the powerful DINOv2 model available under the Apache 2.0 license, employing self-supervised learning to enhance image segmentation and depth estimation. This broader use model encourages further innovation and practical application in the computer vision community, driving progress in the AI industry. FACET for enhanced AI fairness Given the inherent difficulty and risks in ensuring fairness in computer vision, Meta introduced FACET. FACET has been developed to benchmark fairness across computer vision models performing tasks such as detection or classification, considering a wide array of demographic attributes. This revolutionary tool enables a better understanding of potential biases in AI models, helping to address fairness and robustness concerns. Wider implications Preliminary studies indicate performance disparities across some demographic groups within computer vision models. FACET allows researchers to track these divergences and monitor the implementation of corrective measures. Meta actively encourages researchers to use FACET for fairness benchmarking in other visual/multimodal tasks. For instance, the DINOv2 model's performance was analyzed with FACET — facilitating insights into potential biases. (source) P.S. If you like such analysis, I write a free newsletter tracking significant news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

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SAG-AFTRA, the union for US actors, is moving towards a potential strike against video game publishers, it’s announced.[1] Russia builds MSU-270 supercomputer for AI and HPC research.[2] Chuck Schumer has announced that his office will be meeting with top players in the artificial intelligence field later this month. Invited to the upcoming summit are tech megabillionaire Elon Musk, his one-time hypothetical sparring partner Meta CEO Mark Zuckerberg, OpenAI CEO Sam Altman, Google CEO Sundar Pichai, NVIDIA President Jensen Huang, and Alex Karpy, CEO of defense contractor creep Palantir.[3] Google expands AI compute offerings, partnership with Nvidia and more.[4] Sources: [1] https://www.videogameschronicle.com/news/actors-union-sag-aftra-could-launch-video-game-strikes-over-wages-and-ai/ [2] https://www.tomshardware.com/news/russian-400-petaflops-supercomputer-for-ai-comes-online [3] https://gizmodo.com/chuck-schumer-elon-musk-mark-zuckerberg-palantir-nvidia-1850788302 [4] https://www.itworldcanada.com/article/google-expands-ai-compute-offerings-partnership-with-nvidia-and-more/545625 submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

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I'm interested in learning more about how expert systems and explicit knowledge injection by various means can be used to guide and improve RL, both in terms of capability and in terms of reduced training times. I have a hard time finding good resources for this topic. What are some must-read papers on this topic? Are there any good youtube channels or online courses? I'm particularly interested in resources that feature practical implementations submitted by /u/worstthingsonline [link] [comments]  ( 9 min )

When I try to call check_env() from this code: from stable_baselines3.common.env_checker import check_env from agentStable import snakeEnv env = snakeEnv() check_env(env) I get this error: Traceback (most recent call last): File "myDir", line 6, in check_env(env) File "<myDir\Python\Python310\lib\site-packages\stable_baselines3\common\env_checker.py", line 396, in check_env assert isinstance( AssertionError: Your environment must inherit from the gymnasium.Env class cf. https://gymnasium.farama.org/api/env/ ​ Here is my agentStable.py code: import gym from gym import spaces import numpy as np from enum import Enum from collections import namedtuple import numpy as np from colorama import Fore from gameStable import SnakeGameAI, Direction, Point class snakeEnv(gym.Env): met…  ( 10 min )

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Zoomposium with Professor Dr. John-Dylan Haynes: "In search of the code of the brain" In this new episode of our "Zoomposium Series" on the topic of "Brain Research", my colleague Axel Stöcker from the "Blog der großen Fragen" and I have managed to win the well-known and renowned brain researcher and psychologist Professor Dr. John-Dylan Haynes for an interview. John-Dylan Haynes has been a professor of theory and analysis of long-range brain signals at the Bernstein Center for Computational Neuroscience and the Berlin Center for Advanced Neuroimaging (BCAN) at Charité and Humboldt University in Berlin since 2006. There, Professor Haynes and his team are "In Search of the Brain's Code". In order to crack this, larger amounts of data are collected from the functional magnetic resonanc…  ( 10 min )

In training MFRL, which is mostly simulation, why don't we use MBRL instead as the environment is accessible? **Correct me if I misunderstand in any. From my understanding, Model-Free RL (MFRL) is generally used for control tasks where environment is not accessible. It takes a sample of an experience from the environment and uses it to adjust its policy, either policy-based, value-based, and actor-critic. Model-Based RL (MBRL) uses a transition model to optimize the optimal policy like in model predictive control (MPC). I am interested in using RL for control multiple and continuous actions in continuous stochastic environment. For now, I am moving around DDPG. Do you have any suggested algorithm that match to my task? submitted by /u/AnnonymeowCat [link] [comments]  ( 9 min )

In videos like this, it talks about how you need to find the right fit for your data which is shown on a scatter plot. I understand how this works when you have a dataset for something but how does it work when you are trying to train a DQN to play snake (eating apples and getting longer game). I have been struggling to tune my hyperparameters as well as figure out how many hidden neurons and hidden layers I need. I have found that right now 256 hidden neurons and 2 hidden layers works best. Please tell me if this topic has flown completely over my head and I am missing something. Thank you! submitted by /u/MrHank2 [link] [comments]  ( 9 min )

If I wanted to hire an ML/RL student/full-time employee to help my firm develop some FinRL/other RL algorithms, what skills should I be looking for? How "generalized" are RL skills - from what I can tell alot of the RL I see posted here has to do with video games? I've stumbled across FinRL recently and would like to hire some help to develop some FinRL code. What's the market like for RL? I know all the rage is LLM's but how different is RL and does the labour market care about the difference? Based in Canada fyi. Won't be hiring for a few months. submitted by /u/Thrumpwart [link] [comments]  ( 9 min )

Just as there are multiple conventions for defining the Fourier transform, there are multiple conventions for defining the discrete Fourier transform (DFT), better known as the fast Fourier transform (FFT). [1] This post will look at two DFT conventions, one used in Python’s NumPy library, and one used in Mathematica. There are more conventions in […] DFT conventions: NumPy vs Mathematica first appeared on John D. Cook.  ( 6 min )

This sub used to be my go-to place for finding out cool new ML research but sadly it has now become a "generative AI" "AI productisation" circlejerk. I was wondering where people now go to discover new ML research (besides ArXiv of course!) submitted by /u/blabboy [link] [comments]  ( 9 min )

What tech stack, what frameworks should I specially learn and use in my MLE project ? There are so much things mentioned in job offers, what would you suggest me to focus on ? I thought of fine-tuning LLM and deploying it using AWS. I'd prefer this project to be NLP oriented. I read about things like MLFlow, Apache Spar, Kubernetes etc. and don't know what to focus on. PS: I am currently a data scientist, and have recently finished a body pose estimation + action recognition web app, using Python/OpenCV/Mediapipe/Flask/Torch submitted by /u/tflbbl [link] [comments]  ( 9 min )

For a college project I am trying to detect the number of cars in the first 3 rows of a parking lot. Here is my roboflow project page: https://universe.roboflow.com/boaztheostrich/cartest-tyaur As you can see I have been able to get my map score as high as .995 however I am still having difficulty consistently detecting cars in some edge cases. What I am currently testing is increasing the resolution from 1280x720 to 2048. I am new to all of this so any tips or tricks would be greatly appreciated. I am currently using google colab for training although I am considering switching over to vast.ai submitted by /u/johndowlelxdxdxdxdxd [link] [comments]  ( 9 min )

We have it pretty functional but we're a small team so we need more people. https://github.com/apssouza22/chatflow Promo video: https://www.reddit.com/r/AGIunderconstruction/comments/168fsyr/come_build_open_source_natural_language/?utm_source=share&utm_medium=web2x&context=3 submitted by /u/Cold-Explanation-984 [link] [comments]  ( 9 min )

Hey All, I have to read in multiple JSON files with each one containing objects in an array. For each file I want to create a data frame (A matrix might also work) where the rows and columns are just integers pointing to a string. Like this ​ - 0 1 2 0 dsad asd ad 1 asd asd grth 2 ter xc wer ​ Using the same JSON file I also want to process the objects inside the arrays using the dataframe (matrix) above. So the process (pipeline) would be something like: |==> Create Matrix =======>| JSON file =>| | ===> Use matrix to process object. |==> Individual objects ==>| ​ I have been looking through the docs but still unsure how to do this. Should I use a dataframe or a spark matrix? How do I split the objects into parts and also generate the matrix? How do I combine dataframes which isn't joining? Just a point in the right direction would be great. Thanks in advance for this relatively simple question. submitted by /u/atticusfinch975 [link] [comments]  ( 9 min )

I don't have access can somone help me get it please Thank you https://www.worldscientific.com/doi/abs/10.1142/S0218001418560062 submitted by /u/SilenceOfTheUnicorns [link] [comments]  ( 9 min )

I have started learning ML a month ago... Did a foundational Google course and read from some other sources..hav learnt most of the theories....What's the best place to learn algorithms according to you? Any other tips are also welcome submitted by /u/Buri-Buri_zaemon [link] [comments]  ( 9 min )

I know AMD is working on making ROCM support for RDNA 3, would that rival nvidia? and would there be enough support for it to be usable? Nvidia cards are way more expensive and i would like to use it for gaming besides Machine learning for my study. Also, would this be overkill? will an RTX 4070 or an RX 7900 XT also do the job just fine? i am new to ML and won't be using it till early 2024, thank you all for reading. submitted by /u/RepresentativeIll155 [link] [comments]  ( 9 min )

Hi Can someone here please help me with this algorithm? What is the “Boosting” part of the algorithm? To my (limited) understanding XGBoost is an ensemble learning algorithm that uses many decision trees (efficiently), where each tree tries to correct the loss of the previous one. But I’m not sure how this is connected to “Boosting” and then it’s cousin “Bagging” Any intuition that may help me here? submitted by /u/Ok_Reality2341 [link] [comments]  ( 9 min )

I usually peruse the abstract of a paper before deciding on whether to read it or not. However, lately I've started longing for more personalized summaries. I wonder what others think of abstract vs summaries and their preferences of the latter over former ? In your opinion, how far has the field progressed in summarization (https://paperswithcode.com/dataset/scitldr) ? submitted by /u/JurrasicBarf [link] [comments]  ( 9 min )

Hi, we have a paper submission to NeurIPS and we have two reviewers who changed their scores and review content silently by editing the original review comment and score after the discussion period. The edited review comment now discusses entirely different point. We would like to raise this concern to AC but the thing is that we didn’t save the original review comment, and the “revision history” for some reason doesn’t show the previous content, other than the entry that there was previous version. But this revision history overall isn’t inconsistent (showing the last two history after the discussion period, but the ones before the period is not shown) Can reviewers delete their own revision history in OpenReview tool? I don’t know if this is a bug or they deleted them with an intention. submitted by /u/mayasang [link] [comments]  ( 9 min )

I am currently do some predictions on some market data, I'm using both XGBoost and lightGBM (not both at the same time just experimenting using both algorithms). I have around 2500 features and 40k rows of data in my dataset which is being split 75% = train, 12.5% = valid, 12.5% = Test. The balance of the data is massively imbalanced with a binary classification. On training im seeing 0 = 20192 and 1 = 8337. I am not using SMOTE or undersampling but rather using the alogirhms own parameters to combat the Imbalance, for example scale_pos_weight: y_data[0]/y_data[1]. Training is going very well, im using hyperopt tuner to tune my paramets and usually on average get 75% accuracy on testing, training will usually be a little higher such as 77% and valid will be fairly close to test. But the …  ( 10 min )

Hello everyone! I've noticed that most beginner-level tutorials on recommender systems primarily focus on model training, with limited information about deploying them in a production environment. Additionally, the different usage of models in the recall (retrieval) and ranking modules can indeed be confusing for beginners. Recently, I've been working on a recommender system project that encompasses both offline development and online deployment, covering both recall and ranking modules. The entire project is developed using Python and executed on a single laptop. All components are contained within Docker, ensuring no impact on the local environment. The GitHub repo: https://github.com/akiragy/recsys_pipeline You can follow the commands provided in the README to run it. This project primarily utilizes PyTorch, Redis, Elasticsearch, Feast Feature Store, Triton Inference Server, and Flask. PyTorch is used for training the FM model for recall and the DeepFM model for ranking. Redis serves as the store for user terms and vectors, while Elasticsearch is used to create an item term index and a vector index. Redis and Elasticsearch form the recall module. Feast is utilized to store user and item features, while Triton serves as a real-time prediction engine. Feast and Triton form the ranking module. Flask is deployed as the web server, receiving recommendation requests and returning responses. Thanks for checking it out! submitted by /u/Johann_SebastianBach [link] [comments]  ( 9 min )

Benchmark: 🧘 BLISS – a Benchmark for Language Induction from Small Sets https://github.com/taucompling/bliss/ Paper: https://arxiv.org/abs/2308.08253 submitted by /u/nurikolan [link] [comments]  ( 9 min )

We released another preprint on a neuro-symbolic approach called "metacognitive error correction and detection rules" (EDCR). The idea is that if you have a trained neural model, you can symbolically fine tune the results with rules. In this initial study, we apply it to the classification of GPS movement traces. Video: https://www.youtube.com/watch?v=d_OV4lap_rk Preprint: https://arxiv.org/abs/2308.14250 Code: https://github.com/lab-v2/Error-Detection-and-Correction Further information: https://neurosymbolic.asu.edu/metacognition/ In the example below, we show the results for a single class. The rules detect errors by identifying classifications that may be incorrect and then re-assign to a new class. While recall can drop for a given class, we can bound the drop in recall with a hyperparameter - but this is guaranteed to improve precision. This is illustrated in the below figure. We show this approach leads to an overall improvement in accuracy over the base model, including the state-of-the-art. We also examine the effects when encountering classes not seen in the model's training data. We provide theoretical as well as empirical results and believe this approach can be used in other use-cases in the future. ​ https://preview.redd.it/3z0cdp80dulb1.png?width=635&format=png&auto=webp&s=2eff6ce0f2c7b6983dbfbc030f0f7993010a30fb submitted by /u/Neurosymbolic [link] [comments]  ( 9 min )

Hey all, I'm the founder of a generative AI consultancy and we build gen AI powered products for other companies. We've been doing this for 18 months now and I thought I share our learnings - it might help others. ​ It's a never ending battle to keep up with the latest tools and developments. By the time you ship your product it's already using an outdated tech-stack. There are no best-practices yet. You need to make a bet on tools/processes and hope that things won't change much by the time you ship (they will, see point 2). If your generative AI product doesn't have a VC-backed competitor, there will be one soon. In order to win you need one of the two things: either (1) the best distribution or (2) the generative AI component is hidden in your product so others don't/can't copy you. AI researchers / data scientists are suboptimal choice for AI engineering. They're expensive, won't be able to solve most of your problems and likely want to focus on more fundamental problems rather than building products. Software engineers make the best AI engineers. They are able to solve 80% of your problems right away and they are motivated because they can "work in AI". Product designers need to get more technical, AI engineers need to get more product-oriented. The gap currently is too big and this leads to all sorts of problems during product development. Demo bias is real and it makes it 10x harder to deliver something that's in alignment with your client's expectation. Communicating this effectively is a real and underrated skill. There's no such thing as off-the-shelf AI generated content yet. Current tools are not reliable enough, they hallucinate, make up stuff and produce inconsistent results (applies to text, voice, image and video). submitted by /u/BootstrapGuy [link] [comments]  ( 10 min )

Hi everyone, I need a TTS tool that sounds exactly like a human voice. I want to use it to edit some of my YouTube videos. I see a lot of TTS platforms around. Which do you recommend? I hope this isn't too much to ask. I would gladly appreciate it. Thanks in advance. submitted by /u/cessilh1 [link] [comments]  ( 9 min )

You can use the following code if you want to detect the road in real-time in your vehicle/robot : https://github.com/ErkanMilli/3MT-RoadSeg . One of the main problems in road segmentation by using depth was that if a region is flat, such as walls, it may be detected as road. This was already a known phenomenon and to overcome this, surface normal estimation was used. But, SNE requires dense depth images. Instead, we used a multi-task architecture, and used surface normals as an auxiliary loss, which reduced computation time significantly and also we don't need a dense depth image. Only LiDAR (which is sparse in nature) is sufficient. submitted by /u/ozgurerkent [link] [comments]  ( 9 min )

Hi everyone, I am very new to machine learning and statistics, and am currently building an experiment that includes probing the knowledge of a bert-base-uncased model in a fill-mask test, without fine-tuning - just the regular pretrained model. I want to see the models knowledge of certain grammatical notions in English - whether its judgements are similar to those of humans or not :) My point is to give the model inputs like: "what do you call a room filled with socks? you called it a [MASK] filled room", or "a monster who eats rats is called a [MASK] eater", and check the probabilities it gives to the corresponding singular and plural token, e.g. in the first case I want to probe "sock" \ "socks", and in the second case "rat" / "rats". I built a script which does exactly this - pulls…  ( 10 min )

Hi, I’ve been working for a while now on a project to detect points in medical images which are to be classified into 3 different classes, but my UNet really struggles to predict one of the 3 classes (>70% score when excluding this class vs ~30% when not). I have tried putting a separate decoder just for this one class but the results are worse, and I don’t really have other ideas to better my results. Do you have any ideas/techniques to help me improve my results? Thanks ! submitted by /u/maths_and_baguette [link] [comments]  ( 9 min )

Hi everyone. Has anybody taken this course from Stanford https://online.stanford.edu/courses/xcs224w-machine-learning-graphs or any other course in the same online portal? Was it worth it? I am considering to apply. Thanks submitted by /u/Realistic-Bed2658 [link] [comments]  ( 9 min )

Problem I recently did a bit of a career switch from big tech IoT Rust job, into a machine learning research role. For the last few months, I have been working on building out the Recurrent Forward Forward model from Hinton's Forward Forward paper (Fig3): https://arxiv.org/abs/2212.13345 I have an implementation, but have been stuck for the past 4-6 weeks on trying to improve the accuracy. My implementation is only getting 95% test accuracy on MNIST. Hinton and Alex Ororbia (author of this) have been able to achieve high test accuracy (99%+) using this architecture, so I know it is possible. What I have tried I have tried many different things at this point: Different activation functions. Weight initialization. Regularization techniques like transforms, jitter, and dynamic nega…  ( 11 min )

Hi fellow computer scientists and engineers, ​ I've been wondering why do we often have a convolution inside every upsample and downsample block. Well, it makes sense, if you intend to upscale some features and use a bilinear interpolation, then some error can be introduced due to interpolation inaccuracies. This is where convolution layer comes handy to help and support the upscaling. But is this really the reason behind it? Or is there a deeper explanation? ​ Also, just for the sake of curiosity. What if the scale_factor of an upsample block was 1. Should we still keep the convolution layer? or just get rid of all the upsample block since there is no actual "upsampling" being done at least in the context of the tensor dimensions. ​ Thank you :) submitted by /u/Christs_Elite [link] [comments]  ( 9 min )

Is that wrong if a problem doesn't satisfy the Markov property, I cannot solve it with the RL approach either? submitted by /u/nimageran [link] [comments]  ( 9 min )

I am struggling to understand one part of the FAQ of the transformer reinforcement learning library from HuggingFace: What Is the Concern with Negative KL Divergence? If you generate text by purely sampling from the model distribution things work fine in general. But when you use the generate method there are a few caveats because it does not always purely sample depending on the settings which can cause KL-divergence to go negative. Essentially when the active model achieves log_p_token_active < log_p_token_ref we get negative KL-div. This can happen in a several cases: top-k sampling: the model can smooth out the probability distribution causing the top-k tokens having a smaller probability than those of the reference model but they still are selected min_length: this ignores the EOS token until min_length is reached. thus the model can assign a very high log prob to the EOS token and very low prob to all others until min_length is reached batched generation: finished sequences in a batch are padded until all generations are finished. The model can learn to assign very low probabilities to the padding tokens unless they are properly masked or removed.These are just a few examples. Why is negative KL an issue? The total reward R is computed R = r - beta * KL so if the model can learn how to drive KL-divergence negative it effectively gets a positive reward. In many cases it can be much easier to exploit such a bug in the generation than actually learning the reward function. In addition the KL can become arbitrarily small thus the actual reward can be very small compared to it. I understand why the KL-divergence that is computed here is an approximation that can be negative as opposed to the real one. However, I cannot wrap my head around the details of why these specific sampling parameters would lead to negative KL-divergence. Could someone elaborate on these points? submitted by /u/Loud_Appointment_418 [link] [comments]  ( 10 min )

I am using DQN Algorithm and A2C algorithm (Not using any lookaheads to see potential moves and only using self-learning coz my teacher asked me not to look into future combinations and let it play and understand itself) separately to check the performance of the agent and the neural network gives probabilities of the moves in the size of 4096 (64*64) . But the probabilities are decreasing with each and every move performed and they are overfitting to one move which is an invalid move (same case for both dqn and a2c) so in the bellman equation i removed the next reward prediction and put constant value of 1 to check whether it is at least trying to increase the probability for valid moves but that doesnt seems to be the case because it is still giving probability of 1 for an invalid move. and there is also this case where the probabilities are getting so small they are becoming nan values. can someone provide some insights for me to look into submitted by /u/S_U_B_B_U [link] [comments]  ( 9 min )

I see lectures on youtube from UCL+DeppMind on RL spanning from 2015 through 2021. Which one would you say is the best to follow? I've heard many good things about David Silver's lectures, but how do the most recent, 2021, lectures compare? submitted by /u/Practical_Ad_8782 [link] [comments]  ( 9 min )

Math books often use some illustration from the book contents as cover art. When they do, there’s often some mystery to the cover art, and a sense of accomplishment when you get far enough into the book to understand the significance of the cover. (See examples here.) William L. Briggs and Van Emden Henson wrote […] DFT mandalas first appeared on John D. Cook.  ( 5 min )

submitted by /u/nickb [link] [comments]  ( 9 min )

submitted by /u/Neurosymbolic [link] [comments]  ( 9 min )

Elon Musk's X revealed its plans to utilize user data and publicly available information in training AI models. Despite Musk's assurance that only public data will be used, concerns around privacy linger. For expert insights into AI developments, look here first. https://preview.redd.it/mj64uof9rvlb1.png?width=2000&format=png&auto=webp&s=a856ce3e4b6063ebf7a585df3338142defba6323 X's approach to AI training Under the most recent privacy policy, X will harness the personal data it collects and publicly accessible information for its machine learning algorithms. Musk assures only publicly accessible data will be used, safeguarding private user information like DMs. However, with X having disbanded its press operation, more specific details about the data collected and its intended use still need to be provided. Unfolding plans of Musk Despite X's quiet stance on AI, Musk recently launched xAI, aspiring "to understand the true nature of the universe." xAI's homepage discloses plans to sync with X closely, possibly using collected user data to progress the mission. A competitive stance against LinkedIn suggests a possible additional motive for data collection, speculating an enhanced job and education section on X. Despite concerns about selling user data for revenue, concrete evidence is needed to support this argument, reflecting Twitter's previous strategy. (source) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI and tech. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Say goodbye to the black box of deep learning and hello to a new era of transparent, efficient, and ethical AI. Find out how this changes EVERYTHING! https://kinews24.de/stanford-cracks-the-ai-code-the-groundbreaking-law-of-equi-separation ​ submitted by /u/myreddit333 [link] [comments]  ( 9 min )

I'm thinking of creating an AI club at my high school. The problem is, unlike something like math or coding, there aren't many competitions suitable to beginners and not a lot of previous template content to follow. Therefore, I need to forge my own path. I am curious what your ideas are for some engaging, high-school-friendly topics and events to have, especially if we can only meet for 30 minutes a week. Thanks in advance! submitted by /u/0xCUBE [link] [comments]  ( 9 min )

My psychology masters assignments are to be handwritten and hence a somewhat painstaking process. To streamline I was looking for an AI that can guide me on the concepts and understanding of the given psychology subjects. I don't want to use it as a shortcut just a tool for studying and guiding. In accordance with books and Google. Can anyone know of such an AI? submitted by /u/Maddragon0088 [link] [comments]  ( 9 min )

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The stubborn and complex opioid epidemic may finally meet its match—AI. As the crisis continues taking a fearsome toll, experts are turning to advanced technology in their ongoing battle. If you want to stay on top of the latest trends and insights in AI, look here first. https://preview.redd.it/vm23xflorqlb1.jpg?width=1390&format=pjpg&auto=webp&s=212b88fb01eb0f7afaa5011120267ac4ce37ee35 AI’s evolving role in tackling the opioid crisis With a legacy of over 1 million overdose deaths since 1999, the opioid crisis has stubbornly resisted traditional preventive and regulatory measures. The latest AI-fueled developments offer newfound hope. Groundbreaking AI innovations are focusing on identifying individuals at potential risk, monitoring treatment progress, and predicting relapse probabilities. Decoding social media behavior offers an effective outlet for early intervention. More radically, AI-enabled wearable devices are being developed to detect overdose symptoms and automatically deliver lifesaving treatment. AI: A double-edged sword? Despite its promising potential, AI application in this sphere also raises concerns around privacy rights and misinformation. Facial recognition technology could lead to discrimination, while the risk of false data being fed into chatbots causing harm cannot be undermined. Trust in AI and its appropriate deployment will be crucial to ensuring its positive contribution rather than being a dystopian threat. P.S. If you like this kind of analysis, you’ll love my free newsletter that tracks the most relevant news and research in AI and tech. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, a recent study demonstrates the limited efficacy of these embedding algorithms when applied to biomedical knowledge graphs, raising the question of whether knowledge graph embeddings have limitations in biomedical settings. This study aims to apply state-of-the-art knowledge graph embedding models in the context of a recent biomedical knowledge graph, BioKG, and evaluate their performance and potential downstream uses. We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph. Additionally, we provide interpretable predictions through a rule-based method. We demonstrate that knowledge graph embedding models are applicable in practice by evaluating the best-performing model on four tasks that represent real-life polypharmacy situations. Results suggest that knowledge learnt from large biomedical knowledge graphs can be transferred to such downstream use cases. Our code is available at https://github.com/aryopg/biokge.  ( 3 min )

We present 'CongNaMul', a comprehensive dataset designed for various tasks in soybean sprouts image analysis. The CongNaMul dataset is curated to facilitate tasks such as image classification, semantic segmentation, decomposition, and measurement of length and weight. The classification task provides four classes to determine the quality of soybean sprouts: normal, broken, spotted, and broken and spotted, for the development of AI-aided automatic quality inspection technology. For semantic segmentation, images with varying complexity, from single sprout images to images with multiple sprouts, along with human-labelled mask images, are included. The label has 4 different classes: background, head, body, tail. The dataset also provides images and masks for the image decomposition task, including two separate sprout images and their combined form. Lastly, 5 physical features of sprouts (head length, body length, body thickness, tail length, weight) are provided for image-based measurement tasks. This dataset is expected to be a valuable resource for a wide range of research and applications in the advanced analysis of images of soybean sprouts. Also, we hope that this dataset can assist researchers studying classification, semantic segmentation, decomposition, and physical feature measurement in other industrial fields, in evaluating their models. The dataset is available at the authors' repository. (https://bhban.kr/data)  ( 2 min )

In this paper we consider online distributed learning problems. Online distributed learning refers to the process of training learning models on distributed data sources. In our setting a set of agents need to cooperatively train a learning model from streaming data. Differently from federated learning, the proposed approach does not rely on a central server but only on peer-to-peer communications among the agents. This approach is often used in scenarios where data cannot be moved to a centralized location due to privacy, security, or cost reasons. In order to overcome the absence of a central server, we propose a distributed algorithm that relies on a quantized, finite-time coordination protocol to aggregate the locally trained models. Furthermore, our algorithm allows for the use of stochastic gradients during local training. Stochastic gradients are computed using a randomly sampled subset of the local training data, which makes the proposed algorithm more efficient and scalable than traditional gradient descent. In our paper, we analyze the performance of the proposed algorithm in terms of the mean distance from the online solution. Finally, we present numerical results for a logistic regression task.  ( 2 min )

Partitioning a polygonal mesh into meaningful parts can be challenging. Many applications require decomposing such structures for further processing in computer graphics. In the last decade, several methods were proposed to tackle this problem, at the cost of intensive computational times. Recently, machine learning has proven to be effective for the segmentation task on 3D structures. Nevertheless, these state-of-the-art methods are often hardly generalizable and require dividing the learned model into several specific classes of objects to avoid overfitting. We present a data-driven approach leveraging deep learning to encode a mapping function prior to mesh segmentation for multiple applications. Our network reproduces a neighborhood map using our knowledge of the \textsl{Shape Diameter Function} (SDF) method using similarities among vertex neighborhoods. Our approach is resolution-agnostic as we downsample the input meshes and query the full-resolution structure solely for neighborhood contributions. Using our predicted SDF values, we can inject the resulting structure into a graph-cut algorithm to generate an efficient and robust mesh segmentation while considerably reducing the required computation times.  ( 2 min )

Offline Reinforcement Learning (RL) methods leverage previous experiences to learn better policies than the behavior policy used for data collection. In contrast to behavior cloning, which assumes the data is collected from expert demonstrations, offline RL can work with non-expert data and multimodal behavior policies. However, offline RL algorithms face challenges in handling distribution shifts and effectively representing policies due to the lack of online interaction during training. Prior work on offline RL uses conditional diffusion models to represent multimodal behavior in the dataset. Nevertheless, these methods are not tailored toward alleviating the out-of-distribution state generalization. We introduce a novel method, named State Reconstruction for Diffusion Policies (SRDP), incorporating state reconstruction feature learning in the recent class of diffusion policies to address the out-of-distribution generalization problem. State reconstruction loss promotes more descriptive representation learning of states to alleviate the distribution shift incurred by the out-of-distribution (OOD) states. We design a novel 2D Multimodal Contextual Bandit environment to illustrate the OOD generalization of SRDP compared to prior algorithms. In addition, we assess the performance of our model on D4RL continuous control benchmarks, namely the navigation of an 8-DoF ant and forward locomotion of half-cheetah, hopper, and walker2d, achieving state-of-the-art results.  ( 2 min )

In classification and forecasting with tabular data, one often utilizes tree-based models. Those can be competitive with deep neural networks on tabular data [cf. Grinsztajn et al., NeurIPS 2022, arXiv:2207.08815] and, under some conditions, explainable. The explainability depends on the depth of the tree and the accuracy in each leaf of the tree. Decision trees containing leaves with unbalanced accuracy can provide misleading explanations. Low-accuracy leaves give less valid explanations, which could be interpreted as unfairness among explanations. Here, we train a shallow tree with the objective of minimizing the maximum misclassification error across each leaf node. Then, we extend each leaf with a separate tree-based model. The shallow tree provides a global explanation, while the overall statistical performance of the shallow tree with extended leaves improves upon decision trees of unlimited depth trained using classical methods (e.g., CART) and is comparable to state-of-the-art methods (e.g., well-tuned XGBoost).  ( 2 min )

Medical image data are often limited due to the expensive acquisition and annotation process. Hence, training a deep-learning model with only raw data can easily lead to overfitting. One solution to this problem is to augment the raw data with various transformations, improving the model's ability to generalize to new data. However, manually configuring a generic augmentation combination and parameters for different datasets is non-trivial due to inconsistent acquisition approaches and data distributions. Therefore, automatic data augmentation is proposed to learn favorable augmentation strategies for different datasets while incurring large GPU overhead. To this end, we present a novel method, called Dynamic Data Augmentation (DDAug), which is efficient and has negligible computation cost. Our DDAug develops a hierarchical tree structure to represent various augmentations and utilizes an efficient Monte-Carlo tree searching algorithm to update, prune, and sample the tree. As a result, the augmentation pipeline can be optimized for each dataset automatically. Experiments on multiple Prostate MRI datasets show that our method outperforms the current state-of-the-art data augmentation strategies.

In this paper, we first reviewed several biclustering methods that are used to identify the most significant clusters in gene expression data. Here we mainly focused on the SSVD(sparse SVD) method and tried a new sparse penalty named "Prenet penalty" which has been used only in factor analysis to gain sparsity. Then in the simulation study, we tried different types of generated datasets (with different sparsity and dimension) and tried 1-layer approximation then for k-layers which shows the mixed Prenet penalty is very effective for non-overlapped data. Finally, we used some real gene expression data to show the behavior of our methods.

Spiking Neural Networks (SNNs) are a promising research direction for building power-efficient information processing systems, especially for temporal tasks such as speech recognition. In SNNs, delays refer to the time needed for one spike to travel from one neuron to another. These delays matter because they influence the spike arrival times, and it is well-known that spiking neurons respond more strongly to coincident input spikes. More formally, it has been shown theoretically that plastic delays greatly increase the expressivity in SNNs. Yet, efficient algorithms to learn these delays have been lacking. Here, we propose a new discrete-time algorithm that addresses this issue in deep feedforward SNNs using backpropagation, in an offline manner. To simulate delays between consecutive layers, we use 1D convolutions across time. The kernels contain only a few non-zero weights - one per synapse - whose positions correspond to the delays. These positions are learned together with the weights using the recently proposed Dilated Convolution with Learnable Spacings (DCLS). We evaluated our method on three datasets: the Spiking Heidelberg Dataset (SHD), the Spiking Speech Commands (SSC) and its non-spiking version Google Speech Commands v0.02 (GSC) benchmarks, which require detecting temporal patterns. We used feedforward SNNs with two or three hidden fully connected layers, and vanilla leaky integrate-and fire neurons. We showed that fixed random delays help and that learning them helps even more. Furthermore, our method outperformed the state-of-the-art in the three datasets without using recurrent connections and with substantially fewer parameters. Our work demonstrates the potential of delay learning in developing accurate and precise models for temporal data processing. Our code is based on PyTorch / SpikingJelly and available at: https://github.com/Thvnvtos/SNN-delays

Medical applications of machine learning (ML) have experienced a surge in popularity in recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of available data from electronic health records. Models have been proposed to address numerous ICU prediction tasks like the early detection of complications. While authors frequently report state-of-the-art performance, it is challenging to verify claims of superiority. Datasets and code are not always published, and cohort definitions, preprocessing pipelines, and training setups are difficult to reproduce. This work introduces Yet Another ICU Benchmark (YAIB), a modular framework that allows researchers to define reproducible and comparable clinical ML experiments; we offer an end-to-end solution from cohort definition to model evaluation. The framework natively supports most open-access ICU datasets (MIMIC III/IV, eICU, HiRID, AUMCdb) and is easily adaptable to future ICU datasets. Combined with a transparent preprocessing pipeline and extensible training code for multiple ML and deep learning models, YAIB enables unified model development. Our benchmark comes with five predefined established prediction tasks (mortality, acute kidney injury, sepsis, kidney function, and length of stay) developed in collaboration with clinicians. Adding further tasks is straightforward by design. Using YAIB, we demonstrate that the choice of dataset, cohort definition, and preprocessing have a major impact on the prediction performance - often more so than model class - indicating an urgent need for YAIB as a holistic benchmarking tool. We provide our work to the clinical ML community to accelerate method development and enable real-world clinical implementations. Software Repository: https://github.com/rvandewater/YAIB.

We present MedShapeNet, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D surgical instrument models. Prior to the deep learning era, the broad application of statistical shape models (SSMs) in medical image analysis is evidence that shapes have been commonly used to describe medical data. Nowadays, however, state-of-the-art (SOTA) deep learning algorithms in medical imaging are predominantly voxel-based. In computer vision, on the contrary, shapes (including, voxel occupancy grids, meshes, point clouds and implicit surface models) are preferred data representations in 3D, as seen from the numerous shape-related publications in premier vision conferences, such as the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), as well as the increasing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models) in computer vision research. MedShapeNet is created as an alternative to these commonly used shape benchmarks to facilitate the translation of data-driven vision algorithms to medical applications, and it extends the opportunities to adapt SOTA vision algorithms to solve critical medical problems. Besides, the majority of the medical shapes in MedShapeNet are modeled directly on the imaging data of real patients, and therefore it complements well existing shape benchmarks comprising of computer-aided design (CAD) models. MedShapeNet currently includes more than 100,000 medical shapes, and provides annotations in the form of paired data. It is therefore also a freely available repository of 3D models for extended reality (virtual reality - VR, augmented reality - AR, mixed reality - MR) and medical 3D printing. This white paper describes in detail the motivations behind MedShapeNet, the shape acquisition procedures, the use cases, as well as the usage of the online shape search portal: https://medshapenet.ikim.nrw/  ( 4 min )

Adversarial examples (AEs) for DNNs have been shown to be transferable: AEs that successfully fool white-box surrogate models can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable AEs, many of these findings lack explanations and even lead to inconsistent advice. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing little robustness phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates, we attribute it to a trade-off between two predominant factors: model smoothness and gradient similarity. Our investigations focus on their joint effects, rather than their separate correlations with transferability. Through a series of theoretical and empirical analyses, we conjecture that the data distribution shift in adversarial training explains the degradation of gradient similarity. Building on these insights, we explore the impacts of data augmentation and gradient regularization on transferability and identify that the trade-off generally exists in the various training mechanisms, thus building a comprehensive blueprint for the regulation mechanism behind transferability. Finally, we provide a general route for constructing better surrogates to boost transferability which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the crucial role of manipulating surrogate models.  ( 3 min )

In this work, we present MaxViT-UNet, an Encoder-Decoder based hybrid vision transformer (CNN-Transformer) for medical image segmentation. The proposed Hybrid Decoder, based on MaxViT-block, is designed to harness the power of both the convolution and self-attention mechanisms at each decoding stage with a nominal memory and computational burden. The inclusion of multi-axis self-attention, within each decoder stage, significantly enhances the discriminating capacity between the object and background regions, thereby helping in improving the segmentation efficiency. In the Hybrid Decoder block, the fusion process commences by integrating the upsampled lower-level decoder features, obtained through transpose convolution, with the skip-connection features derived from the hybrid encoder. Subsequently, the fused features undergo refinement through the utilization of a multi-axis attention mechanism. The proposed decoder block is repeated multiple times to progressively segment the nuclei regions. Experimental results on MoNuSeg18 and MoNuSAC20 dataset demonstrates the effectiveness of the proposed technique. Our MaxViT-UNet outperformed the previous CNN-based (UNet) and Transformer-based (Swin-UNet) techniques by a considerable margin on both of the standard datasets. The following github (https://github.com/PRLAB21/MaxViT-UNet) contains the implementation and trained weights.  ( 2 min )

Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy. While off-the-shelf billion-scale datasets for image generation are available, collecting similar video data of the same scale is still challenging. Also, training a video diffusion model is computationally much more expensive than its image counterpart. In this work, we explore finetuning a pretrained image diffusion model with video data as a practical solution for the video synthesis task. We find that naively extending the image noise prior to video noise prior in video diffusion leads to sub-optimal performance. Our carefully designed video noise prior leads to substantially better performance. Extensive experimental validation shows that our model, Preserve Your Own Correlation (PYoCo), attains SOTA zero-shot text-to-video results on the UCF-101 and MSR-VTT benchmarks. It also achieves SOTA video generation quality on the small-scale UCF-101 benchmark with a $10\times$ smaller model using significantly less computation than the prior art.  ( 2 min )

Personalized prediction is a machine learning approach that predicts a person's future observations based on their past labeled observations and is typically used for sequential tasks, e.g., to predict daily mood ratings. When making personalized predictions, a model can combine two types of trends: (a) trends shared across people, i.e., person-generic trends, such as being happier on weekends, and (b) unique trends for each person, i.e., person-specific trends, such as a stressful weekly meeting. Mixed effect models are popular statistical models to study both trends by combining person-generic and person-specific parameters. Though linear mixed effect models are gaining popularity in machine learning by integrating them with neural networks, these integrations are currently limited to linear person-specific parameters: ruling out nonlinear person-specific trends. In this paper, we propose Neural Mixed Effect (NME) models to optimize nonlinear person-specific parameters anywhere in a neural network in a scalable manner. NME combines the efficiency of neural network optimization with nonlinear mixed effects modeling. Empirically, we observe that NME improves performance across six unimodal and multimodal datasets, including a smartphone dataset to predict daily mood and a mother-adolescent dataset to predict affective state sequences where half the mothers experience at least moderate symptoms of depression. Furthermore, we evaluate NME for two model architectures, including for neural conditional random fields (CRF) to predict affective state sequences where the CRF learns nonlinear person-specific temporal transitions between affective states. Analysis of these person-specific transitions on the mother-adolescent dataset shows interpretable trends related to the mother's depression symptoms.  ( 3 min )

Distance-based clustering and classification are widely used in various fields to group mixed numeric and categorical data. In many algorithms, a predefined distance measurement is used to cluster data points based on their dissimilarity. While there exist numerous distance-based measures for data with pure numerical attributes and several ordered and unordered categorical metrics, an efficient and accurate distance for mixed-type data that utilizes the continuous and discrete properties simulatenously is an open problem. Many metrics convert numerical attributes to categorical ones or vice versa. They handle the data points as a single attribute type or calculate a distance between each attribute separately and add them up. We propose a metric called KDSUM that uses mixed kernels to measure dissimilarity, with cross-validated optimal bandwidth selection. We demonstrate that KDSUM is a shrinkage method from existing mixed-type metrics to a uniform dissimilarity metric, and improves clustering accuracy when utilized in existing distance-based clustering algorithms on simulated and real-world datasets containing continuous-only, categorical-only, and mixed-type data.  ( 2 min )

Despite the widespread utilization of Gaussian process models for versatile nonparametric modeling, they exhibit limitations in effectively capturing abrupt changes in function smoothness and accommodating relationships with heteroscedastic errors. Addressing these shortcomings, the heteroscedastic Gaussian process (HeGP) regression seeks to introduce flexibility by acknowledging the variability of residual variances across covariates in the regression model. In this work, we extend the HeGP concept, expanding its scope beyond regression tasks to encompass classification and state-space models. To achieve this, we propose a novel framework where the Gaussian process is coupled with a covariate-induced precision matrix process, adopting a mixture formulation. This approach enables the modeling of heteroscedastic covariance functions across covariates. To mitigate the computational challenges posed by sampling, we employ variational inference to approximate the posterior and facilitate posterior predictive modeling. Additionally, our training process leverages an EM algorithm featuring closed-form M-step updates to efficiently evaluate the heteroscedastic covariance function. A notable feature of our model is its consistent performance on multivariate responses, accommodating various types (continuous or categorical) seamlessly. Through a combination of simulations and real-world applications in climatology, we illustrate the model's prowess and advantages. By overcoming the limitations of traditional Gaussian process models, our proposed framework offers a robust and versatile tool for a wide array of applications.  ( 2 min )

The Fourier neural operator (FNO) is a powerful technique for learning surrogate maps for partial differential equation (PDE) solution operators. For many real-world applications, which often require high-resolution data points, training time and memory usage are significant bottlenecks. While there are mixed-precision training techniques for standard neural networks, those work for real-valued datatypes on finite dimensions and therefore cannot be directly applied to FNO, which crucially operates in the (complex-valued) Fourier domain and in function spaces. On the other hand, since the Fourier transform is already an approximation (due to discretization error), we do not need to perform the operation at full precision. In this work, we (i) profile memory and runtime for FNO with full and mixed-precision training, (ii) conduct a study on the numerical stability of mixed-precision training of FNO, and (iii) devise a training routine which substantially decreases training time and memory usage (up to 34%), with little or no reduction in accuracy, on the Navier-Stokes and Darcy flow equations. Combined with the recently proposed tensorized FNO (Kossaifi et al., 2023), the resulting model has far better performance while also being significantly faster than the original FNO.  ( 2 min )

In reinforcement learning (RL), key components of many algorithms are the exploration strategy and replay buffer. These strategies regulate what environment data is collected and trained on and have been extensively studied in the RL literature. In this paper, we investigate the impact of these components in the context of generalisation in multi-task RL. We investigate the hypothesis that collecting and training on more diverse data from the training environments will improve zero-shot generalisation to new tasks. We motivate mathematically and show empirically that generalisation to tasks that are "reachable'' during training is improved by increasing the diversity of transitions in the replay buffer. Furthermore, we show empirically that this same strategy also shows improvement for generalisation to similar but "unreachable'' tasks which could be due to improved generalisation of the learned latent representations.  ( 2 min )

Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on. Due to the fact that the probability distributions cannot be averaged over different models straightforwardly, the current time series model ensemble methods cannot be directly applied to improve the robustness and accuracy of forecasting. To address this issue, we propose pTSE, a multi-model distribution ensemble method for probabilistic forecasting based on Hidden Markov Model (HMM). pTSE only takes off-the-shelf outputs from member models without requiring further information about each model. Besides, we provide a complete theoretical analysis of pTSE to prove that the empirical distribution of time series subject to an HMM will converge to the stationary distribution almost surely. Experiments on benchmarks show the superiority of pTSE overall member models and competitive ensemble methods.  ( 2 min )

We present the Multi-Modal Discussion Transformer (mDT), a novel multi-modal graph-based transformer model for detecting hate speech in online social networks, such as Reddit discussions. In contrast to traditional comment-only methods, our approach to labelling a comment as hate speech involves a holistic analysis of text and images grounded in the discussion context. This is done by leveraging graph transformers to capture the contextual relationships in the entire discussion surrounding a comment and grounding the interwoven fusion layers that combine individual comments' text and image embeddings instead of processing modalities separately. We compare the performance of our model to baselines that only process individual comments and conduct extensive ablation studies. To evaluate our work, we present a new dataset, HatefulDiscussions, comprising complete multi-modal discussions from multiple online communities on Reddit. We conclude with future work for multimodal solutions to deliver social value in online contexts, arguing that capturing a holistic view of a conversation significantly advances the effort to detect anti-social behaviour.  ( 2 min )

Maximum mean discrepancy (MMD) flows suffer from high computational costs in large scale computations. In this paper, we show that MMD flows with Riesz kernels $K(x,y) = - \Vert x-y\Vert^r$, $r \in (0,2)$ have exceptional properties which allow their efficient computation. We prove that the MMD of Riesz kernels coincides with the MMD of their sliced version. As a consequence, the computation of gradients of MMDs can be performed in the one-dimensional setting. Here, for $r=1$, a simple sorting algorithm can be applied to reduce the complexity from $O(MN+N^2)$ to $O((M+N)\log(M+N))$ for two measures with $M$ and $N$ support points. As another interesting follow-up result, the MMD of compactly supported measures can be estimated from above and below by the Wasserstein-1 distance. For the implementations we approximate the gradient of the sliced MMD by using only a finite number $P$ of slices. We show that the resulting error has complexity $O(\sqrt{d/P})$, where $d$ is the data dimension. These results enable us to train generative models by approximating MMD gradient flows by neural networks even for image applications. We demonstrate the efficiency of our model by image generation on MNIST, FashionMNIST and CIFAR10.  ( 2 min )

The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within 5G networks by utilizing real-world network data that is accessible to mobile network operators (MNOs). In particular, (i) we present an analytical formulation of the user-plane latency as a Hypoexponential distribution, which is validated by means of a comparative analysis with empirical measurements, and (ii) we conduct experimental results of probabilistic regression, anomaly detection, and predictive forecasting leveraging on emerging domains in Machine Learning (ML), such as Bayesian Learning (BL) and Machine Learning on Graphs (GML). We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events. Our results provide valuable insights into the efficacy of predictive algorithms in practical applications.  ( 2 min )

Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge. To address this, we propose DNAGPT, a generalized DNA pre-training model trained on over 200 billion base pairs from all mammals. By enhancing the classic GPT model with a binary classification task (DNA sequence order), a numerical regression task (guanine-cytosine content prediction), and a comprehensive token language, DNAGPT can handle versatile DNA analysis tasks while processing both sequence and numerical data. Our evaluation of genomic signal and region recognition, mRNA abundance regression, and artificial genomes generation tasks demonstrates DNAGPT's superior performance compared to existing models designed for specific downstream tasks, benefiting from pre-training using the newly designed model structure.  ( 2 min )

The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices. Quantizing the parameters and operations to lower bit-precision offers substantial memory and energy savings for neural network inference, facilitating the use of DNNs on edge computing platforms. Recent efforts at quantizing DNNs have employed a range of techniques encompassing progressive quantization, step-size adaptation, and gradient scaling. This paper proposes a new quantization approach for mixed precision convolutional neural networks (CNNs) targeting edge-computing. Our method establishes a new pareto frontier in model accuracy and memory footprint demonstrating a range of quantized models, delivering best-in-class accuracy below 4.3 MB of weights (wgts.) and activations (acts.). Our main contributions are: (i) hardware-aware heterogeneous differentiable quantization with tensor-sliced learned precision, (ii) targeted gradient modification for wgts. and acts. to mitigate quantization errors, and (iii) a multi-phase learning schedule to address instability in learning arising from updates to the learned quantizer and model parameters. We demonstrate the effectiveness of our techniques on the ImageNet dataset across a range of models including EfficientNet-Lite0 (e.g., 4.14MB of wgts. and acts. at 67.66% accuracy) and MobileNetV2 (e.g., 3.51MB wgts. and acts. at 65.39% accuracy).  ( 2 min )

We study the use of binary activated neural networks as interpretable and explainable predictors in the context of regression tasks on tabular data; more specifically, we provide guarantees on their expressiveness, present an approach based on the efficient computation of SHAP values for quantifying the relative importance of the features, hidden neurons and even weights. As the model's simplicity is instrumental in achieving interpretability, we propose a greedy algorithm for building compact binary activated networks. This approach doesn't need to fix an architecture for the network in advance: it is built one layer at a time, one neuron at a time, leading to predictors that aren't needlessly complex for a given task.  ( 2 min )

Realistic aircraft trajectory models are useful in the design and validation of air traffic management (ATM) systems. Models of aircraft operated under instrument flight rules (IFR) require capturing the variability inherent in how aircraft follow standard flight procedures. The variability in aircraft behavior varies among flight stages. In this paper, we propose a probabilistic model that can learn the variability from the procedural data and flight tracks collected from radar surveillance data. For each segment, a Gaussian mixture model is used to learn the deviations of aircraft trajectories from their procedures. Given new procedures, we can generate synthetic trajectories by sampling a series of deviations from the trained Gaussian distributions and reconstructing the aircraft trajectory using the deviations and the procedures. We extend this method to capture pairwise correlations between aircraft and show how a pairwise model can be used to generate traffic involving an arbitrary number of aircraft. We demonstrate the proposed models on the arrival tracks and procedures of the John F. Kennedy International Airport. The distributional similarity between the original and the synthetic trajectory dataset was evaluated using the Jensen-Shannon divergence between the empirical distributions of different variables. We also provide qualitative analyses of the synthetic trajectories generated from the models.  ( 2 min )

We seek to give users precise control over diffusion-based image generation by modeling complex scenes as sequences of layers, which define the desired spatial arrangement and visual attributes of objects in the scene. Collage Diffusion harmonizes the input layers to make objects fit together -- the key challenge involves minimizing changes in the positions and key visual attributes of the input layers while allowing other attributes to change in the harmonization process. We ensure that objects are generated in the correct locations by modifying text-image cross-attention with the layers' alpha masks. We preserve key visual attributes of input layers by learning specialized text representations per layer and by extending ControlNet to operate on layers. Layer input allows users to control the extent of image harmonization on a per-object basis, and users can even iteratively edit individual objects in generated images while keeping other objects fixed. By leveraging the rich information present in layer input, Collage Diffusion generates globally harmonized images that maintain desired object characteristics better than prior approaches.  ( 2 min )

Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair graph learning simply assumes that attributes of all nodes are available for model training and then makes fair predictions. In practice, however, the attributes of some nodes might not be accessible due to missing data or privacy concerns, which makes fair graph learning even more challenging. In this paper, we propose FairAC, a fair attribute completion method, to complement missing information and learn fair node embeddings for graphs with missing attributes. FairAC adopts an attention mechanism to deal with the attribute missing problem and meanwhile, it mitigates two types of unfairness, i.e., feature unfairness from attributes and topological unfairness due to attribute completion. FairAC can work on various types of homogeneous graphs and generate fair embeddings for them and thus can be applied to most downstream tasks to improve their fairness performance. To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems. Experimental results on benchmark datasets show that our method achieves better fairness performance with less sacrifice in accuracy, compared with the state-of-the-art methods of fair graph learning. Code is available at: https://github.com/donglgcn/FairAC.  ( 2 min )

Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requires training a neural network for each representation and selecting the best one based on the validation score, which is very time-consuming. This work eliminates this bottleneck by selecting representations based on the Gromov-Wasserstein Discrepancy (GWD) between raw events and their representation. It is about 200 times faster to compute than training a neural network and preserves the task performance ranking of event representations across multiple representations, network backbones, datasets, and tasks. Thus finding representations with high task scores is equivalent to finding representations with a low GWD. We use this insight to, for the first time, perform a hyperparameter search on a large family of event representations, revealing new and powerful representations that exceed the state-of-the-art. Our optimized representations outperform existing representations by 1.7 mAP on the 1 Mpx dataset and 0.3 mAP on the Gen1 dataset, two established object detection benchmarks, and reach a 3.8% higher classification score on the mini N-ImageNet benchmark. Moreover, we outperform state-of-the-art by 2.1 mAP on Gen1 and state-of-the-art feed-forward methods by 6.0 mAP on the 1 Mpx datasets. This work opens a new unexplored field of explicit representation optimization for event-based learning.  ( 3 min )

We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor. The dataset encompasses 897k images of wine labels and 824k reviews of wines curated from the Vivino platform. It has over 350k unique vintages, annotated with year, region, rating, alcohol percentage, price, and grape composition. We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment with 256 participants who were asked to rank wines based on their similarity in flavor, resulting in more than 5k pairwise flavor distances. We propose a low-dimensional concept embedding algorithm that combines human experience with automatic machine similarity kernels. We demonstrate that this shared concept embedding space improves upon separate embedding spaces for coarse flavor classification (alcohol percentage, country, grape, price, rating) and aligns with the intricate human perception of flavor.  ( 2 min )

It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To achieve this goal, the robot needs to have a comprehensive understanding of the 3D structure and semantics of the scene. In this work, we present $\textbf{GNFactor}$, a visual behavior cloning agent for multi-task robotic manipulation with $\textbf{G}$eneralizable $\textbf{N}$eural feature $\textbf{F}$ields. GNFactor jointly optimizes a generalizable neural field (GNF) as a reconstruction module and a Perceiver Transformer as a decision-making module, leveraging a shared deep 3D voxel representation. To incorporate semantics in 3D, the reconstruction module utilizes a vision-language foundation model ($\textit{e.g.}$, Stable Diffusion) to distill rich semantic information into the deep 3D voxel. We evaluate GNFactor on 3 real robot tasks and perform detailed ablations on 10 RLBench tasks with a limited number of demonstrations. We observe a substantial improvement of GNFactor over current state-of-the-art methods in seen and unseen tasks, demonstrating the strong generalization ability of GNFactor. Our project website is https://yanjieze.com/GNFactor/ .  ( 2 min )

Hypergraphs are important for processing data with higher-order relationships involving more than two entities. In scenarios where explicit hypergraphs are not readily available, it is desirable to infer a meaningful hypergraph structure from the node features to capture the intrinsic relations within the data. However, existing methods either adopt simple pre-defined rules that fail to precisely capture the distribution of the potential hypergraph structure, or learn a mapping between hypergraph structures and node features but require a large amount of labelled data, i.e., pre-existing hypergraph structures, for training. Both restrict their applications in practical scenarios. To fill this gap, we propose a novel smoothness prior that enables us to design a method to infer the probability for each potential hyperedge without labelled data as supervision. The proposed prior indicates features of nodes in a hyperedge are highly correlated by the features of the hyperedge containing them. We use this prior to derive the relation between the hypergraph structure and the node features via probabilistic modelling. This allows us to develop an unsupervised inference method to estimate the probability for each potential hyperedge via solving an optimisation problem that has an analytical solution. Experiments on both synthetic and real-world data demonstrate that our method can learn meaningful hypergraph structures from data more efficiently than existing hypergraph structure inference methods.

Face de-identification methods have been proposed to preserve users' privacy by obscuring their faces. These methods, however, can degrade the quality of photos, and they usually do not preserve the utility of faces, i.e., their age, gender, pose, and facial expression. Recently, GANs, such as StyleGAN, have been proposed, which generate realistic, high-quality imaginary faces. In this paper, we investigate the use of StyleGAN in generating de-identified faces through style mixing. We examined this de-identification method for preserving utility and privacy by implementing several face detection, verification, and identification attacks and conducting a user study. The results from our extensive experiments, human evaluation, and comparison with two state-of-the-art methods, i.e., CIAGAN and DeepPrivacy, show that StyleGAN performs on par or better than these methods, preserving users' privacy and images' utility. In particular, the results of the machine learning-based experiments show that StyleGAN0-4 preserves utility better than CIAGAN and DeepPrivacy while preserving privacy at the same level. StyleGAN0-3 preserves utility at the same level while providing more privacy. In this paper, for the first time, we also performed a carefully designed user study to examine both privacy and utility-preserving properties of StyleGAN0-3, 0-4, and 0-5, as well as CIAGAN and DeepPrivacy from the human observers' perspectives. Our statistical tests showed that participants tend to verify and identify StyleGAN0-5 images more easily than DeepPrivacy images. All the methods but StyleGAN0-5 had significantly lower identification rates than CIAGAN. Regarding utility, as expected, StyleGAN0-5 performed significantly better in preserving some attributes. Among all methods, on average, participants believe gender has been preserved the most while naturalness has been preserved the least.

Real-time predictive modelling with desired accuracy is highly expected in industrial artificial intelligence (IAI), where neural networks play a key role. Neural networks in IAI require powerful, high-performance computing devices to operate a large number of floating point data. Based on stochastic configuration networks (SCNs), this paper proposes a new randomized learner model, termed stochastic configuration machines (SCMs), to stress effective modelling and data size saving that are useful and valuable for industrial applications. Compared to SCNs and random vector functional-link (RVFL) nets with binarized implementation, the model storage of SCMs can be significantly compressed while retaining favourable prediction performance. Besides the architecture of the SCM learner model and its learning algorithm, as an important part of this contribution, we also provide a theoretical basis on the learning capacity of SCMs by analysing the model's complexity. Experimental studies are carried out over some benchmark datasets and three industrial applications. The results demonstrate that SCM has great potential for dealing with industrial data analytics.

Emotion recognition in conversation (ERC) has received increasing attention from researchers due to its wide range of applications.As conversation has a natural graph structure,numerous approaches used to model ERC based on graph convolutional networks (GCNs) have yielded significant results.However,the aggregation approach of traditional GCNs suffers from the node information redundancy problem,leading to node discriminant information loss.Additionally,single-layer GCNs lack the capacity to capture long-range contextual information from the graph. Furthermore,the majority of approaches are based on textual modality or stitching together different modalities, resulting in a weak ability to capture interactions between modalities. To address these problems, we present the relational bilevel aggregation graph convolutional network (RBA-GCN), which consists of three modules: the graph generation module (GGM), similarity-based cluster building module (SCBM) and bilevel aggregation module (BiAM). First, GGM constructs a novel graph to reduce the redundancy of target node information.Then,SCBM calculates the node similarity in the target node and its structural neighborhood, where noisy information with low similarity is filtered out to preserve the discriminant information of the node. Meanwhile, BiAM is a novel aggregation method that can preserve the information of nodes during the aggregation process. This module can construct the interaction between different modalities and capture long-range contextual information based on similarity clusters. On both the IEMOCAP and MELD datasets, the weighted average F1 score of RBA-GCN has a 2.17$\sim$5.21\% improvement over that of the most advanced method.Our code is available at https://github.com/luftmenscher/RBA-GCN and our article with the same name has been published in IEEE/ACM Transactions on Audio,Speech,and Language Processing,vol.31,2023

Neural force fields (NFFs) have gained prominence in computational chemistry as surrogate models, superseding quantum-chemistry calculations in ab initio molecular dynamics. The prevalent benchmark for NFFs has been the MD17 dataset and its subsequent extension. These datasets predominantly comprise geometries from the equilibrium region of the ground electronic state potential energy surface, sampling from direct adiabatic dynamics. However, many chemical reactions entail significant molecular deformations, notably bond breaking. We demonstrate the constrained distribution of internal coordinates and energies in the MD17 datasets, underscoring their inadequacy for representing systems undergoing chemical reactions. Addressing this sampling limitation, we introduce the xxMD (Extended Excited-state Molecular Dynamics) dataset, derived from non-adiabatic dynamics. This dataset encompasses energies and forces ascertained from both multireference wave function theory and density functional theory. Furthermore, its nuclear configuration spaces authentically depict chemical reactions, making xxMD a more chemically relevant dataset. Our re-assessment of equivariant models on the xxMD datasets reveals notably higher mean absolute errors than those reported for MD17 and its variants. This observation underscores the challenges faced in crafting a generalizable NFF model with extrapolation capability. Our proposed xxMD-CASSCF and xxMD-DFT datasets are available at https://github.com/zpengmei/xxMD.

Plain text has become a prevalent interface for text-to-image synthesis. However, its limited customization options hinder users from accurately describing desired outputs. For example, plain text makes it hard to specify continuous quantities, such as the precise RGB color value or importance of each word. Furthermore, creating detailed text prompts for complex scenes is tedious for humans to write and challenging for text encoders to interpret. To address these challenges, we propose using a rich-text editor supporting formats such as font style, size, color, and footnote. We extract each word's attributes from rich text to enable local style control, explicit token reweighting, precise color rendering, and detailed region synthesis. We achieve these capabilities through a region-based diffusion process. We first obtain each word's region based on attention maps of a diffusion process using plain text. For each region, we enforce its text attributes by creating region-specific detailed prompts and applying region-specific guidance, and maintain its fidelity against plain-text generation through region-based injections. We present various examples of image generation from rich text and demonstrate that our method outperforms strong baselines with quantitative evaluations.

Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible. While global minimization of the PDE residual over the network parameters works well for boundary value problems, catastrophic forgetting impairs the applicability of this approach to initial value problems (IVPs). In an alternative local-in-time approach, the optimization problem can be converted into an ordinary differential equation (ODE) on the network parameters and the solution propagated forward in time; however, we demonstrate that current methods based on this approach suffer from two key issues. First, following the ODE produces an uncontrolled growth in the conditioning of the problem, ultimately leading to unacceptably large numerical errors. Second, as the ODE methods scale cubically with the number of model parameters, they are restricted to small neural networks, significantly limiting their ability to represent intricate PDE initial conditions and solutions. Building on these insights, we develop Neural IVP, an ODE based IVP solver which prevents the network from getting ill-conditioned and runs in time linear in the number of parameters, enabling us to evolve the dynamics of challenging PDEs with neural networks.

Large Language Models (LLMs) have shown promise in automated program reasoning, a crucial aspect of many security tasks. However, existing LLM architectures for code are often borrowed from other domains like natural language processing, raising concerns about their generalization and robustness to unseen code. A key generalization challenge is to incorporate the knowledge of code semantics, including control and data flow, into the LLM architectures. Drawing inspiration from examples of convolution layers exploiting translation symmetry, we explore how code symmetries can enhance LLM architectures for program analysis and modeling. We present a rigorous group-theoretic framework that formally defines code symmetries as semantics-preserving transformations and provides techniques for precisely reasoning about symmetry preservation within LLM architectures. Using this framework, we introduce a novel variant of self-attention that preserves program symmetries, demonstrating its effectiveness in generalization and robustness through detailed experimental evaluations across different binary and source code analysis tasks. Overall, our code symmetry framework offers rigorous and powerful reasoning techniques that can guide the future development of specialized LLMs for code and advance LLM-guided program reasoning tasks.

We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to prioritize the parsimonious partial differential equation (PDE) that sufficiently governs noisy spatial-temporal observed data with few reliable terms. Since the naive use of the BIC for model selection has been known to yield an undesirable overfitted PDE, the UBIC penalizes the found PDE not only by its complexity but also the quantified uncertainty, derived from the model supports' coefficient of variation in a probabilistic view. We also introduce physics-informed neural network learning as a simulation-based approach to further validate the selected PDE flexibly against the other discovered PDE. Numerical results affirm the successful application of the UBIC in identifying the true governing PDE. Additionally, we reveal an interesting effect of denoising the observed data on improving the trade-off between the BIC score and model complexity. Code is available at https://github.com/Pongpisit-Thanasutives/UBIC.

Compiled software is delivered as executable binary code. Developers write source code to express the software semantics, but the compiler converts it to a binary format that the CPU can directly execute. Therefore, binary code analysis is critical to applications in reverse engineering and computer security tasks where source code is not available. However, unlike source code and natural language that contain rich semantic information, binary code is typically difficult for human engineers to understand and analyze. While existing work uses AI models to assist source code analysis, few studies have considered binary code. In this paper, we propose a COntrastive learning Model for Binary cOde Analysis, or COMBO, that incorporates source code and comment information into binary code during representation learning. Specifically, we present three components in COMBO: (1) a primary contrastive learning method for cold-start pre-training, (2) a simplex interpolation method to incorporate source code, comments, and binary code, and (3) an intermediate representation learning algorithm to provide binary code embeddings. Finally, we evaluate the effectiveness of the pre-trained representations produced by COMBO using three indicative downstream tasks relating to binary code: algorithmic functionality classification, binary code similarity, and vulnerability detection. Our experimental results show that COMBO facilitates representation learning of binary code visualized by distribution analysis, and improves the performance on all three downstream tasks by 5.45% on average compared to state-of-the-art large-scale language representation models. To the best of our knowledge, COMBO is the first language representation model that incorporates source code, binary code, and comments into contrastive code representation learning and unifies multiple tasks for binary code analysis.

Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative for full fine-tuning so as to adapt pre-trained vision models to downstream tasks, which only tunes a small number of parameters while freezing the vast majority ones to ease storage burden and optimization difficulty. However, existing PEFT methods introduce trainable parameters to the same positions across different tasks depending solely on human heuristics and neglect the domain gaps. To this end, we study where to introduce and how to allocate trainable parameters by proposing a novel Sensitivity-aware visual Parameter-efficient fine-Tuning (SPT) scheme, which adaptively allocates trainable parameters to task-specific important positions given a desired tunable parameter budget. Specifically, our SPT first quickly identifies the sensitive parameters that require tuning for a given task in a data-dependent way. Next, our SPT further boosts the representational capability for the weight matrices whose number of sensitive parameters exceeds a pre-defined threshold by utilizing existing structured tuning methods, e.g., LoRA [23] or Adapter [22], to replace directly tuning the selected sensitive parameters (unstructured tuning) under the budget. Extensive experiments on a wide range of downstream recognition tasks show that our SPT is complementary to the existing PEFT methods and largely boosts their performance, e.g., SPT improves Adapter with supervised pre-trained ViT-B/16 backbone by 4.2% and 1.4% mean Top-1 accuracy, reaching SOTA performance on FGVC and VTAB-1k benchmarks, respectively. Source code is at https://github.com/ziplab/SPT

Statistical and stochastic analysis based on thermodynamics has been the main analysis framework for stochastic global optimization. Recently, appearing quantum annealing or quantum tunneling algorithm for global optimization, we require a new researching framework for global optimization algorithms. In this paper, we provide the analysis for quantization-based optimization based on the Schr\"odinger equation to reveal what property in quantum mechanics enables global optimization. We present that the tunneling effect derived by the Schr\"odinger equation in quantization-based optimization enables to escape of a local minimum. Additionally, we confirm that this tunneling effect is the same property included in quantum mechanics-based global optimization. Experiments with standard multi-modal benchmark functions represent that the proposed analysis is valid.

Many learning algorithms used as normative models in neuroscience or as candidate approaches for learning on neuromorphic chips learn by contrasting one set of network states with another. These Contrastive Learning (CL) algorithms are traditionally implemented with rigid, temporally non-local, and periodic learning dynamics that could limit the range of physical systems capable of harnessing CL. In this study, we build on recent work exploring how CL might be implemented by biological or neurmorphic systems and show that this form of learning can be made temporally local, and can still function even if many of the dynamical requirements of standard training procedures are relaxed. Thanks to a set of general theorems corroborated by numerical experiments across several CL models, our results provide theoretical foundations for the study and development of CL methods for biological and neuromorphic neural networks.

Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. We present a large-scale floating vehicle dataset of per-street segment traffic information, Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities (MeTS-10), available for 10 global cities with a 15-minute resolution for collection periods ranging between 108 and 361 days in 2019-2021 and covering more than 1500 square kilometers per metropolitan area. MeTS-10 features traffic speed information at all street levels from main arterials to local streets for Antwerp, Bangkok, Barcelona, Berlin, Chicago, Istanbul, London, Madrid, Melbourne and Moscow. The dataset leverages the industrial-scale floating vehicle Traffic4cast data with speeds and vehicle counts provided in a privacy-preserving spatio-temporal aggregation. We detail the efficient matching approach mapping the data to the OpenStreetMap road graph. We evaluate the dataset by comparing it with publicly available stationary vehicle detector data (for Berlin, London, and Madrid) and the Uber traffic speed dataset (for Barcelona, Berlin, and London). The comparison highlights the differences across datasets in spatio-temporal coverage and variations in the reported traffic caused by the binning method. MeTS-10 enables novel, city-wide analysis of mobility and traffic patterns for ten major world cities, overcoming current limitations of spatially sparse vehicle detector data. The large spatial and temporal coverage offers an opportunity for joining the MeTS-10 with other datasets, such as traffic surveys in traffic planning studies or vehicle detector data in traffic control settings.

In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing. Meanwhile, the structure of neural networks converged back to simpler representations based on piecewise constant and piecewise linear functions such as the Rectified Linear Unit (ReLU), which became the most commonly used type of activation function in neural networks. That made certain types of network structure $\unicode{x2014}$such as the typical fully-connected feedforward neural network$\unicode{x2014}$ amenable to analysis through polyhedral theory and to the application of methodologies such as Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) for a variety of purposes. In this paper, we survey the main topics emerging from this fast-paced area of work, which bring a fresh perspective to understanding neural networks in more detail as well as to applying linear optimization techniques to train, verify, and reduce the size of such networks.

Bayesian model reduction provides an efficient approach for comparing the performance of all nested sub-models of a model, without re-evaluating any of these sub-models. Until now, Bayesian model reduction has been applied mainly in the computational neuroscience community on simple models. In this paper, we formulate and apply Bayesian model reduction to perform principled pruning of Bayesian neural networks, based on variational free energy minimization. Direct application of Bayesian model reduction, however, gives rise to approximation errors. Therefore, a novel iterative pruning algorithm is presented to alleviate the problems arising with naive Bayesian model reduction, as supported experimentally on the publicly available UCI datasets for different inference algorithms. This novel parameter pruning scheme solves the shortcomings of current state-of-the-art pruning methods that are used by the signal processing community. The proposed approach has a clear stopping criterion and minimizes the same objective that is used during training. Next to these benefits, our experiments indicate better model performance in comparison to state-of-the-art pruning schemes.

One major challenge in machine learning applications is coping with mismatches between the datasets used in the development and those obtained in real-world applications. These mismatches may lead to inaccurate predictions and errors, resulting in poor product quality and unreliable systems. In this study, we propose StyleDiff to inform developers of the differences between the two datasets for the steady development of machine learning systems. Using disentangled image spaces obtained from recently proposed generative models, StyleDiff compares the two datasets by focusing on attributes in the images and provides an easy-to-understand analysis of the differences between the datasets. The proposed StyleDiff performs in $O (d N\log N)$, where $N$ is the size of the datasets and $d$ is the number of attributes, enabling the application to large datasets. We demonstrate that StyleDiff accurately detects differences between datasets and presents them in an understandable format using, for example, driving scenes datasets.

In autonomous driving, data augmentation is commonly used for improving 3D object detection. The most basic methods include insertion of copied objects and rotation and scaling of the entire training frame. Numerous variants have been developed as well. The existing methods, however, are considerably limited when compared to the variety of the real world possibilities. In this work, we develop a diversified and realistic augmentation method that can flexibly construct a whole-body object, freely locate and rotate the object, and apply self-occlusion and external-occlusion accordingly. To improve the diversity of the whole-body object construction, we develop an iterative method that stochastically combines multiple objects observed from the real world into a single object. Unlike the existing augmentation methods, the constructed objects can be randomly located and rotated in the training frame because proper occlusions can be reflected to the whole-body objects in the final step. Finally, proper self-occlusion at each local object level and external-occlusion at the global frame level are applied using the Hidden Point Removal (HPR) algorithm that is computationally efficient. HPR is also used for adaptively controlling the point density of each object according to the object's distance from the LiDAR. Experiment results show that the proposed DR.CPO algorithm is data-efficient and model-agnostic without incurring any computational overhead. Also, DR.CPO can improve mAP performance by 2.08% when compared to the best 3D detection result known for KITTI dataset. The code is available at https://github.com/SNU-DRL/DRCPO.git

Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths. However, melanoma diagnoses by pathologists shows low interrater reliability. As melanoma is a cancer of the melanocyte, there is a clear need to develop a melanocytic cell segmentation tool that is agnostic to pathologist variability and automates pixel-level annotation. Gigapixel-level pathologist labeling, however, is impractical. Herein, we propose a means to train deep neural networks for melanocytic cell segmentation from hematoxylin and eosin (H&E) stained sections and paired immunohistochemistry (IHC) of adjacent tissue sections, achieving a mean IOU of 0.64 despite imperfect ground-truth labels.

This paper studies a cooperative multi-agent multi-armed stochastic bandit problem where agents operate asynchronously -- agent pull times and rates are unknown, irregular, and heterogeneous -- and face the same instance of a K-armed bandit problem. Agents can share reward information to speed up the learning process at additional communication costs. We propose ODC, an on-demand communication protocol that tailors the communication of each pair of agents based on their empirical pull times. ODC is efficient when the pull times of agents are highly heterogeneous, and its communication complexity depends on the empirical pull times of agents. ODC is a generic protocol that can be integrated into most cooperative bandit algorithms without degrading their performance. We then incorporate ODC into the natural extensions of UCB and AAE algorithms and propose two communication-efficient cooperative algorithms. Our analysis shows that both algorithms are near-optimal in regret.

The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsic characteristics (e.g., discontinuity and no viable information of subgradients) impede its development for several decades. Even if there is an impressive body of work on designing DNNs with continuous activation functions that can be deemed as surrogates of the step function, it is still in the possession of some advantageous properties, such as complete robustness to outliers and being capable of attaining the best learning-theoretic guarantee of predictive accuracy. Hence, in this paper, we aim to train DNNs with the step function used as an activation function (dubbed as 0/1 DNNs). We first reformulate 0/1 DNNs as an unconstrained optimization problem and then solve it by a block coordinate descend (BCD) method. Moreover, we acquire closed-form solutions for sub-problems of BCD as well as its convergence properties. Furthermore, we also integrate $\ell_{2,0}$-regularization into 0/1 DNN to accelerate the training process and compress the network scale. As a result, the proposed algorithm has a high performance on classifying MNIST and Fashion-MNIST datasets. As a result, the proposed algorithm has a desirable performance on classifying MNIST, FashionMNIST, Cifar10, and Cifar100 datasets.

In spatial regression models, spatial heterogeneity may be considered with either continuous or discrete specifications. The latter is related to delineation of spatially connected regions with homogeneous relationships between variables (spatial regimes). Although various regionalization algorithms have been proposed and studied in the field of spatial analytics, methods to optimize spatial regimes have been largely unexplored. In this paper, we propose two new algorithms for spatial regime delineation, two-stage K-Models and Regional-K-Models. We also extend the classic Automatic Zoning Procedure to spatial regression context. The proposed algorithms are applied to a series of synthetic datasets and two real-world datasets. Results indicate that all three algorithms achieve superior or comparable performance to existing approaches, while the two-stage K-Models algorithm largely outperforms existing approaches on model fitting, region reconstruction, and coefficient estimation. Our work enriches the spatial analytics toolbox to explore spatial heterogeneous processes.

The main goal of Few-Shot learning algorithms is to enable learning from small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (MAML). The main idea behind this method is to learn the shared universal weights of a meta-model, which are then adapted for specific tasks. However, the method suffers from over-fitting and poorly quantifies uncertainty due to limited data size. Bayesian approaches could, in principle, alleviate these shortcomings by learning weight distributions in place of point-wise weights. Unfortunately, previous modifications of MAML are limited due to the simplicity of Gaussian posteriors, MAML-like gradient-based weight updates, or by the same structure enforced for universal and adapted weights. In this paper, we propose a novel framework for Bayesian MAML called BayesianHMAML, which employs Hypernetworks for weight updates. It learns the universal weights point-wise, but a probabilistic structure is added when adapted for specific tasks. In such a framework, we can use simple Gaussian distributions or more complicated posteriors induced by Continuous Normalizing Flows.

Federated learning (FL) enables multiple clients to collaboratively train a global model without disclosing their data. Previous researches often require training the complete model parameters. However, the emergence of powerful pre-trained models makes it possible to achieve higher performance with fewer learnable parameters in FL. In this paper, we propose a federated adaptive prompt tuning algorithm, FedAPT, for multi-domain collaborative image classification with powerful foundation models, like CLIP. Compared with direct federated prompt tuning, our core idea is to adaptively unlock specific domain knowledge for each test sample in order to provide them with personalized prompts. To implement this idea, we design an adaptive prompt tuning module, which consists of a meta prompt, an adaptive network, and some keys. The server randomly generates a set of keys and assigns a unique key to each client. Then all clients cooperatively train the global adaptive network and meta prompt with the local datasets and the frozen keys. Ultimately, the global aggregation model can assign a personalized prompt to CLIP based on the domain features of each test sample. We perform extensive experiments on two multi-domain image classification datasets across two different settings - supervised and unsupervised. The results show that FedAPT can achieve better performance with less than 10\% of the number of parameters of the fully trained model, and the global model can perform well in diverse client domains simultaneously.

This study demonstrates the feasibility of point cloud-based proactive link quality prediction for millimeter-wave (mmWave) communications. Previous studies have proposed machine learning-based methods to predict received signal strength for future time periods using time series of depth images to mitigate the line-of-sight (LOS) path blockage by pedestrians in mmWave communication. However, these image-based methods have limited applicability due to privacy concerns as camera images may contain sensitive information. This study proposes a point cloud-based method for mmWave link quality prediction and demonstrates its feasibility through experiments. Point clouds represent three-dimensional (3D) spaces as a set of points and are sparser and less likely to contain sensitive information than camera images. Additionally, point clouds provide 3D position and motion information, which is necessary for understanding the radio propagation environment involving pedestrians. This study designs the mmWave link quality prediction method and conducts realistic indoor experiments, where the link quality fluctuates significantly due to human blockage, using commercially available IEEE 802.11ad-based 60 GHz wireless LAN devices and Kinect v2 RGB-D camera and Velodyne VLP-16 light detection and ranging (LiDAR) for point cloud acquisition. The experimental results showed that our proposed method can predict future large attenuation of mmWave received signal strength and throughput induced by the LOS path blockage by pedestrians with comparable or superior accuracy to image-based prediction methods. Hence, our point cloud-based method can serve as a viable alternative to image-based methods.

The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contribution of a given data point from a training procedure. Federated Unlearning (FU) consists in extending MU to unlearn a given client's contribution from a federated training routine. Current FU approaches are generally not scalable, and do not come with sound theoretical quantification of the effectiveness of unlearning. In this work we present Informed Federated Unlearning (IFU), a novel efficient and quantifiable FU approach. Upon unlearning request from a given client, IFU identifies the optimal FL iteration from which FL has to be reinitialized, with unlearning guarantees obtained through a randomized perturbation mechanism. The theory of IFU is also extended to account for sequential unlearning requests. Experimental results on different tasks and dataset show that IFU leads to more efficient unlearning procedures as compared to basic re-training and state-of-the-art FU approaches.

Quality-sensitive applications of machine learning (ML) require quality assurance (QA) by humans before the predictions of an ML model can be deployed. QA for ML (QA4ML) interfaces require users to view a large amount of data and perform many interactions to correct errors made by the ML model. An optimized user interface (UI) can significantly reduce interaction costs. While UI optimization can be informed by user studies evaluating design options, this approach is not scalable because there are typically numerous small variations that can affect the efficiency of a QA4ML interface. Hence, we propose using simulation to evaluate and aid the optimization of QA4ML interfaces. In particular, we focus on simulating the combined effects of human intelligence in initiating appropriate interaction commands and machine intelligence in providing algorithmic assistance for accelerating QA4ML processes. As QA4ML is usually labor-intensive, we use the simulated task completion time as the metric for UI optimization under different interface and algorithm setups. We demonstrate the usage of this UI design method in several QA4ML applications.

Current methods for embedding-based query answering over incomplete Knowledge Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by learning patterns from the data, and lack the complementary ability to do deductive reasoning, which requires the application of domain knowledge to infer further information. To address this shortcoming, we investigate the problem of incorporating ontologies into embedding-based query answering models by defining the task of embedding-based ontology-mediated query answering. We propose various integration strategies into prominent representatives of embedding models that involve (1) different ontology-driven data augmentation techniques and (2) adaptation of the loss function to enforce the ontology axioms. We design novel benchmarks for the considered task based on the LUBM and the NELL KGs and evaluate our methods on them. The achieved improvements in the setting that requires both inductive and deductive reasoning are from 20% to 55% in HITS@3.

We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states. The method has no free parameters and requires no explicit orthogonalization of the different states, instead transforming the problem of finding excited states of a given system into that of finding the ground state of an expanded system. Expected values of arbitrary observables can be calculated, including off-diagonal expectations between different states such as the transition dipole moment. Although the method is entirely general, it works particularly well in conjunction with recent work on using neural networks as variational Ansatze for many-electron systems, and we show that by combining this method with the FermiNet and Psiformer Ansatze we can accurately recover vertical excitation energies and oscillator strengths on molecules as large as benzene. Beyond the examples on molecules presented here, we expect this technique will be of great interest for applications of variational quantum Monte Carlo to atomic, nuclear and condensed matter physics.

Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers. In this work, we propose two novel architectures of self-interpretable image classifiers that first explain, and then predict (as opposed to post-hoc explanations) by harnessing the visual correspondences between a query image and exemplars. Our models consistently improve (by 1 to 4 points) on out-of-distribution (OOD) datasets while performing marginally worse (by 1 to 2 points) on in-distribution tests than ResNet-50 and a $k$-nearest neighbor classifier (kNN). Via a large-scale, human study on ImageNet and CUB, our correspondence-based explanations are found to be more useful to users than kNN explanations. Our explanations help users more accurately reject AI's wrong decisions than all other tested methods. Interestingly, for the first time, we show that it is possible to achieve complementary human-AI team accuracy (i.e., that is higher than either AI-alone or human-alone), in ImageNet and CUB image classification tasks.

Semantic segmentation in autonomous driving has been undergoing an evolution from sparse point segmentation to dense voxel segmentation, where the objective is to predict the semantic occupancy of each voxel in the concerned 3D space. The dense nature of the prediction space has rendered existing efficient 2D-projection-based methods (e.g., bird's eye view, range view, etc.) ineffective, as they can only describe a subspace of the 3D scene. To address this, we propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively and a PointOcc model to process them efficiently. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system for more fine-grained modeling of nearer areas. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane. Finally, we obtain the features of each point by aggregating its projected features on each of the processed TPV planes without the need for any post-processing. Extensive experiments on both 3D occupancy prediction and LiDAR segmentation benchmarks demonstrate that the proposed PointOcc achieves state-of-the-art performance with much faster speed. Specifically, despite only using LiDAR, PointOcc significantly outperforms all other methods, including multi-modal methods, with a large margin on the OpenOccupancy benchmark. Code: https://github.com/wzzheng/PointOcc.

Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. Moreover, the parameter server needs to wait for the slowest client (i.e., straggler, which may have the largest model size, lowest computing capability or worst network condition) to upload parameters, which may significantly degrade the communication efficiency. Commonly-used client selection methods such as partial client selection would lead to the waste of computing resources and weaken the generalization of the global model. To tackle this problem, along a different line, in this paper, we advocate the approach of model parameter dropout instead of client selection, and accordingly propose a novel framework of Federated learning scheme with Differential parameter Dropout (FedDD). FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints. Specifically, the dropout rate allocation is formulated as a convex optimization problem, taking system heterogeneity, data heterogeneity, and model heterogeneity among clients into consideration. The uploaded parameter selection strategy prioritizes on eliciting important parameters for uploading to speedup convergence. Furthermore, we theoretically analyze the convergence of the proposed FedDD scheme. Extensive performance evaluations demonstrate that the proposed FedDD scheme can achieve outstanding performances in both communication efficiency and model convergence, and also possesses a strong generalization capability to data of rare classes.

Synthesis from linear temporal logic (LTL) specifications provides assured controllers for systems operating in stochastic and potentially adversarial environments. Automatic synthesis tools, however, require a model of the environment to construct controllers. In this work, we introduce a model-free reinforcement learning (RL) approach to derive controllers from given LTL specifications even when the environment is completely unknown. We model the problem as a stochastic game (SG) between the controller and the adversarial environment; we then learn optimal control strategies that maximize the probability of satisfying the LTL specifications against the worst-case environment behavior. We first construct a product game using the deterministic parity automaton (DPA) translated from the given LTL specification. By deriving distinct rewards and discount factors from the acceptance condition of the DPA, we reduce the maximization of the worst-case probability of satisfying the LTL specification into the maximization of a discounted reward objective in the product game; this enables the use of model-free RL algorithms to learn an optimal controller strategy. To deal with the common scalability problems when the number of sets defining the acceptance condition of the DPA (usually referred as colors), is large, we propose a lazy color generation method where distinct rewards and discount factors are utilized only when needed, and an approximate method where the controller eventually focuses on only one color. In several case studies, we show that our approach is scalable to a wide range of LTL formulas, significantly outperforming existing methods for learning controllers from LTL specifications in SGs.

Generative models for images have gained significant attention in computer vision and natural language processing due to their ability to generate realistic samples from complex data distributions. To leverage the advances of image-based generative models for the time series domain, we propose a two-dimensional image representation for time series, the Extended Intertemporal Return Plot (XIRP). Our approach captures the intertemporal time series dynamics in a scale-invariant and invertible way, reducing training time and improving sample quality. We benchmark synthetic XIRPs obtained by an off-the-shelf Wasserstein GAN with gradient penalty (WGAN-GP) to other image representations and models regarding similarity and predictive ability metrics. Our novel, validated image representation for time series consistently and significantly outperforms a state-of-the-art RNN-based generative model regarding predictive ability. Further, we introduce an improved stochastic inversion to substantially improve simulation quality regardless of the representation and provide the prospect of transfer potentials in other domains.

Neural networks have gained much interest because of their effectiveness in many applications. However, their mathematical properties are generally not well understood. If there is some underlying geometric structure inherent to the data or to the function to approximate, it is often desirable to take this into account in the design of the neural network. In this work, we start with a non-autonomous ODE and build neural networks using a suitable, structure-preserving, numerical time-discretisation. The structure of the neural network is then inferred from the properties of the ODE vector field. Besides injecting more structure into the network architectures, this modelling procedure allows a better theoretical understanding of their behaviour. We present two universal approximation results and demonstrate how to impose some particular properties on the neural networks. A particular focus is on 1-Lipschitz architectures including layers that are not 1-Lipschitz. These networks are expressive and robust against adversarial attacks, as shown for the CIFAR-10 and CIFAR-100 datasets.

We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics. Such programs are common in High Energy Physics due to the presence of branching processes and clustering-based analysis. Thus differentiating such programs can open the way for gradient based optimization in the context of detector design optimization, simulator tuning, or data analysis and reconstruction optimization. We discuss several possible gradient estimation strategies, including the recent Stochastic AD method, and compare them in simplified detector design experiments. In doing so we develop, to the best of our knowledge, the first fully differentiable branching program.

Multi-output Gaussian processes (MOGPs) have been introduced to deal with multiple tasks by exploiting the correlations between different outputs. Generally, MOGPs models assume a flat correlation structure between the outputs. However, such a formulation does not account for more elaborate relationships, for instance, if several replicates were observed for each output (which is a typical setting in biological experiments). This paper proposes an extension of MOGPs for hierarchical datasets (i.e. datasets for which the relationships between observations can be represented within a tree structure). Our model defines a tailored kernel function accounting for hierarchical structures in the data to capture different levels of correlations while leveraging the introduction of latent variables to express the underlying dependencies between outputs through a dedicated kernel. This latter feature is expected to significantly improve scalability as the number of tasks increases. An extensive experimental study involving both synthetic and real-world data from genomics and motion capture is proposed to support our claims.

The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would otherwise hinder physics-quality data taking. Although the existing ECAL DQM system has been continuously updated to respond to new problems, it remains one step behind newer and unforeseen issues. Using unsupervised deep learning, a real-time autoencoder-based anomaly detection system is developed that is able to detect ECAL anomalies unseen in past data. After accounting for spatial variations in the response of the ECAL and the temporal evolution of anomalies, the new system is able to efficiently detect anomalies while maintaining an estimated false discovery rate between $10^{-2}$ to $10^{-4}$, beating existing benchmarks by about two orders of magnitude. The real-world performance of the system is validated using anomalies found in 2018 and 2022 LHC collision data. Additionally, first results from deploying the autoencoder-based system in the CMS online DQM workflow for the ECAL barrel during Run 3 of the LHC are presented, showing its promising performance in detecting obscure issues that could have been missed in the existing DQM system.

A machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy exploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution over time. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement. As an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes.

A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. When designed well, these systems promise more objective decisions while saving large amounts of resources and freeing up human time. However, when ADM systems are not designed well, they can lead to unfair decisions which discriminate against societal groups. The downstream effects of ADMs critically depend on the decisions made during the systems' design and implementation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these design decisions are made implicitly, without knowing exactly how they will influence the final system. It is therefore important to make explicit the decisions made during the design of ADM systems and understand how these decisions affect the fairness of the resulting system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit design decisions into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible "universes" of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand variability and robustness of algorithmic fairness using an exemplary case study of predicting public health coverage of vulnerable populations for potential interventions. Our results illustrate how decisions during the design of a machine learning system can have surprising effects on its fairness and how to detect these effects using multiverse analysis.

Probabilistic Diffusion Models (PDMs) have recently emerged as a very promising class of generative models, achieving high performance in natural image generation. However, their performance relative to non-natural images, like radar-based satellite data, remains largely unknown. Generating large amounts of synthetic (and especially labelled) satellite data is crucial to implement deep-learning approaches for the processing and analysis of (interferometric) satellite aperture radar data. Here, we leverage PDMs to generate several radar-based satellite image datasets. We show that PDMs succeed in generating images with complex and realistic structures, but that sampling time remains an issue. Indeed, accelerated sampling strategies, which work well on simple image datasets like MNIST, fail on our radar datasets. We provide a simple and versatile open-source https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation to train, sample and evaluate PDMs using any dataset on a single GPU.

We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation of text models in high-, medium-, and low-resource languages. Each question is based on a short passage from the Flores-200 dataset and has four multiple-choice answers. The questions were carefully curated to discriminate between models with different levels of general language comprehension. The English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. We use this dataset to evaluate the capabilities of multilingual masked language models (MLMs) and large language models (LLMs). We present extensive results and find that despite significant cross-lingual transfer in English-centric LLMs, much smaller MLMs pretrained on balanced multilingual data still understand far more languages. We also observe that larger vocabulary size and conscious vocabulary construction correlate with better performance on low-resource languages. Overall, Belebele opens up new avenues for evaluating and analyzing the multilingual capabilities of NLP systems.

Since its inception in "Attention Is All You Need", transformer architecture has led to revolutionary advancements in NLP. The attention layer within the transformer admits a sequence of input tokens $X$ and makes them interact through pairwise similarities computed as softmax$(XQK^\top X^\top)$, where $(K,Q)$ are the trainable key-query parameters. In this work, we establish a formal equivalence between the optimization geometry of self-attention and a hard-margin SVM problem that separates optimal input tokens from non-optimal tokens using linear constraints on the outer-products of token pairs. This formalism allows us to characterize the implicit bias of 1-layer transformers optimized with gradient descent: (1) Optimizing the attention layer with vanishing regularization, parameterized by $(K,Q)$, converges in direction to an SVM solution minimizing the nuclear norm of the combined parameter $W=KQ^\top$. Instead, directly parameterizing by $W$ minimizes a Frobenius norm objective. We characterize this convergence, highlighting that it can occur toward locally-optimal directions rather than global ones. (2) Complementing this, we prove the local/global directional convergence of gradient descent under suitable geometric conditions. Importantly, we show that over-parameterization catalyzes global convergence by ensuring the feasibility of the SVM problem and by guaranteeing a benign optimization landscape devoid of stationary points. (3) While our theory applies primarily to linear prediction heads, we propose a more general SVM equivalence that predicts the implicit bias with nonlinear heads. Our findings are applicable to arbitrary datasets and their validity is verified via experiments. We also introduce several open problems and research directions. We believe these findings inspire the interpretation of transformers as a hierarchy of SVMs that separates and selects optimal tokens.

Federated learning (FL) involves several devices that collaboratively train a shared model without transferring their local data. FL reduces the communication overhead, making it a promising learning method in UAV-enhanced wireless networks with scarce energy resources. Despite the potential, implementing FL in UAV-enhanced networks is challenging, as conventional UAV placement methods that maximize coverage increase the FL delay significantly. Moreover, the uncertainty and lack of a priori information about crucial variables, such as channel quality, exacerbate the problem. In this paper, we first analyze the statistical characteristics of a UAV-enhanced wireless sensor network (WSN) with energy harvesting. We then develop a model and solution based on the multi-objective multi-armed bandit theory to maximize the network coverage while minimizing the FL delay. Besides, we propose another solution that is particularly useful with large action sets and strict energy constraints at the UAVs. Our proposal uses a scalarized best-arm identification algorithm to find the optimal arms that maximize the ratio of the expected reward to the expected energy cost by sequentially eliminating one or more arms in each round. Then, we derive the upper bound on the error probability of our multi-objective and cost-aware algorithm. Numerical results show the effectiveness of our approach.

In prediction-based Neural Architecture Search (NAS), performance indicators derived from graph convolutional networks have shown significant success. These indicators, achieved by representing feed-forward structures as component graphs through one-hot encoding, face a limitation: their inability to evaluate architecture performance across varying search spaces. In contrast, handcrafted performance indicators (zero-shot NAS), which use the same architecture with random initialization, can generalize across multiple search spaces. Addressing this limitation, we propose a novel approach for zero-shot NAS using deep learning. Our method employs Fourier sum of sines encoding for convolutional kernels, enabling the construction of a computational feed-forward graph with a structure similar to the architecture under evaluation. These encodings are learnable and offer a comprehensive view of the architecture's topological information. An accompanying multi-layer perceptron (MLP) then ranks these architectures based on their encodings. Experimental results show that our approach surpasses previous methods using graph convolutional networks in terms of correlation on the NAS-Bench-201 dataset and exhibits a higher convergence rate. Moreover, our extracted feature representation trained on each NAS-Benchmark is transferable to other NAS-Benchmarks, showing promising generalizability across multiple search spaces. The code is available at: https://github.com/minh1409/DFT-NPZS-NAS

This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization. Although the current versions of ADMM algorithm provide promising numerical results in producing solutions that are close to optimal for many convex and non-convex optimization problems, it remains unclear if they can converge to a stationary point for weakly convex and locally non-smooth functions. Through our analysis using the Moreau envelope function, we demonstrate that MADM can indeed converge to a stationary point under mild conditions. Our analysis also includes computing the bounds on the amount of change in the dual variable update step by relating the gradient of the Moreau envelope function to the proximal function. Furthermore, the results of our numerical experiments indicate that our method is faster and more robust than widely-used approaches.

With the growing imbalance between limited medical resources and escalating demands, AI-based clinical tasks have become paramount. Medication recommendation, as a sub-domain, aims to amalgamate longitudinal patient history with medical knowledge, assisting physicians in prescribing safer and more accurate medication combinations. Existing methods overlook the inherent long-tail distribution in medical data, lacking balanced representation between head and tail data, which leads to sub-optimal model performance. To address this challenge, we introduce StratMed, a model that incorporates an innovative relevance stratification mechanism. It harmonizes discrepancies in data long-tail distribution and strikes a balance between the safety and accuracy of medication combinations. Specifically, we first construct a pre-training method using deep learning networks to obtain entity representation. After that, we design a pyramid-like data stratification method to obtain more generalized entity relationships by reinforcing the features of unpopular entities. Based on this relationship, we designed two graph structures to express medication precision and safety at the same level to obtain visit representations. Finally, the patient's historical clinical information is fitted to generate medication combinations for the current health condition. Experiments on the MIMIC-III dataset demonstrate that our method has outperformed current state-of-the-art methods in four evaluation metrics (including safety and accuracy).

Our study reveals new theoretical insights into over-smoothing and feature over-correlation in deep graph neural networks. We show the prevalence of invariant subspaces, demonstrating a fixed relative behavior that is unaffected by feature transformations. Our work clarifies recent observations related to convergence to a constant state and a potential over-separation of node states, as the amplification of subspaces only depends on the spectrum of the aggregation function. In linear scenarios, this leads to node representations being dominated by a low-dimensional subspace with an asymptotic convergence rate independent of the feature transformations. This causes a rank collapse of the node representations, resulting in over-smoothing when smooth vectors span this subspace, and over-correlation even when over-smoothing is avoided. Guided by our theory, we propose a sum of Kronecker products as a beneficial property that can provably prevent over-smoothing, over-correlation, and rank collapse. We empirically extend our insights to the non-linear case, demonstrating the inability of existing models to capture linearly independent features.

Contact is at the core of robotic manipulation. At times, it is desired (e.g. manipulation and grasping), and at times, it is harmful (e.g. when avoiding obstacles). However, traditional path planning algorithms focus solely on collision-free paths, limiting their applicability in contact-rich tasks. To address this limitation, we propose the domain of Language-Conditioned Path Planning, where contact-awareness is incorporated into the path planning problem. As a first step in this domain, we propose Language-Conditioned Collision Functions (LACO) a novel approach that learns a collision function using only a single-view image, language prompt, and robot configuration. LACO predicts collisions between the robot and the environment, enabling flexible, conditional path planning without the need for manual object annotations, point cloud data, or ground-truth object meshes. In both simulation and the real world, we demonstrate that LACO can facilitate complex, nuanced path plans that allow for interaction with objects that are safe to collide, rather than prohibiting any collision.

Offline Reinforcement Learning (RL) is structured to derive policies from static trajectory data without requiring real-time environment interactions. Recent studies have shown the feasibility of framing offline RL as a sequence modeling task, where the sole aim is to predict actions based on prior context using the transformer architecture. However, the limitation of this single task learning approach is its potential to undermine the transformer model's attention mechanism, which should ideally allocate varying attention weights across different tokens in the input context for optimal prediction. To address this, we reformulate offline RL as a multi-objective optimization problem, where the prediction is extended to states and returns. We also highlight a potential flaw in the trajectory representation used for sequence modeling, which could generate inaccuracies when modeling the state and return distributions. This is due to the non-smoothness of the action distribution within the trajectory dictated by the behavioral policy. To mitigate this issue, we introduce action space regions to the trajectory representation. Our experiments on D4RL benchmark locomotion tasks reveal that our propositions allow for more effective utilization of the attention mechanism in the transformer model, resulting in performance that either matches or outperforms current state-of-the art methods.

In this article, the optimal sample complexity of learning the underlying interaction/dependencies of a Linear Dynamical System (LDS) over a Directed Acyclic Graph (DAG) is studied. The sample complexity of learning a DAG's structure is well-studied for static systems, where the samples of nodal states are independent and identically distributed (i.i.d.). However, such a study is less explored for DAGs with dynamical systems, where the nodal states are temporally correlated. We call such a DAG underlying an LDS as \emph{dynamical} DAG (DDAG). In particular, we consider a DDAG where the nodal dynamics are driven by unobserved exogenous noise sources that are wide-sense stationary (WSS) in time but are mutually uncorrelated, and have the same {power spectral density (PSD)}. Inspired by the static settings, a metric and an algorithm based on the PSD matrix of the observed time series are proposed to reconstruct the DDAG. The equal noise PSD assumption can be relaxed such that identifiability conditions for DDAG reconstruction are not violated. For the LDS with WSS (sub) Gaussian exogenous noise sources, it is shown that the optimal sample complexity (or length of state trajectory) needed to learn the DDAG is $n=\Theta(q\log(p/q))$, where $p$ is the number of nodes and $q$ is the maximum number of parents per node. To prove the sample complexity upper bound, a concentration bound for the PSD estimation is derived, under two different sampling strategies. A matching min-max lower bound using generalized Fano's inequality also is provided, thus showing the order optimality of the proposed algorithm.

This paper addresses the problem of localization, which is inherently non-convex and non-smooth in a federated setting where the data is distributed across a multitude of devices. Due to the decentralized nature of federated environments, distributed learning becomes essential for scalability and adaptability. Moreover, these environments are often plagued by outlier data, which presents substantial challenges to conventional methods, particularly in maintaining estimation accuracy and ensuring algorithm convergence. To mitigate these challenges, we propose a method that adopts an $L_1$-norm robust formulation within a distributed sub-gradient framework, explicitly designed to handle these obstacles. Our approach addresses the problem in its original form, without resorting to iterative simplifications or approximations, resulting in enhanced computational efficiency and improved estimation accuracy. We demonstrate that our method converges to a stationary point, highlighting its effectiveness and reliability. Through numerical simulations, we confirm the superior performance of our approach, notably in outlier-rich environments, which surpasses existing state-of-the-art localization methods.

In the rapidly evolving field of automatic echocardiographic analysis and interpretation, automatic view classification is a critical yet challenging task, owing to the inherent complexity and variability of echocardiographic data. This study presents ECHOcardiography VIew Classification with Out-of-Distribution dEtection (ECHO-VICODE), a novel deep learning-based framework that effectively addresses this challenge by training to classify 31 classes, surpassing previous studies and demonstrating its capacity to handle a wide range of echocardiographic views. Furthermore, ECHO-VICODE incorporates an integrated out-of-distribution (OOD) detection function, leveraging the relative Mahalanobis distance to effectively identify 'near-OOD' instances commonly encountered in echocardiographic data. Through extensive experimentation, we demonstrated the outstanding performance of ECHO-VICODE in terms of view classification and OOD detection, significantly reducing the potential for errors in echocardiographic analyses. This pioneering study significantly advances the domain of automated echocardiography analysis and exhibits promising prospects for substantial applications in extensive clinical research and practice.

Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential patient outcomes. Despite active research on the subject, several gaps remain: 1) proposed forecasting models have become outdated due to quick influx of advanced machine learning models (ML), 2) amount of multivariable input data has been limited and 3) discrete performance metrics have been rarely reported. In this study, we document the performance of a set of advanced ML models in forecasting ED occupancy 24 hours ahead. We use electronic health record data from a large, combined ED with an extensive set of explanatory variables, including the availability of beds in catchment area hospitals, traffic data from local observation stations, weather variables, etc. We show that N-BEATS and LightGBM outpeform benchmarks with 11 % and 9 % respective improvements and that DeepAR predicts next day crowding with an AUC of 0.76 (95 % CI 0.69-0.84). To the best of our knowledge, this is the first study to document the superiority of LightGBM and N-BEATS over statistical benchmarks in the context of ED forecasting.

The rapid development of blockchain has led to more and more funding pouring into the cryptocurrency market, which also attracted cybercriminals' interest in recent years. The Ponzi scheme, an old-fashioned fraud, is now popular on the blockchain, causing considerable financial losses to many crypto-investors. A few Ponzi detection methods have been proposed in the literature, most of which detect a Ponzi scheme based on its smart contract source code or opcode. The contract-code-based approach, while achieving very high accuracy, is not robust: first, the source codes of a majority of contracts on Ethereum are not available, and second, a Ponzi developer can fool a contract-code-based detection model by obfuscating the opcode or inventing a new profit distribution logic that cannot be detected (since these models were trained on existing Ponzi logics only). A transaction-based approach could improve the robustness of detection because transactions, unlike smart contracts, are harder to be manipulated. However, the current transaction-based detection models achieve fairly low accuracy. We address this gap in the literature by developing new detection models that rely only on the transactions, hence guaranteeing the robustness, and moreover, achieve considerably higher Accuracy, Precision, Recall, and F1-score than existing transaction-based models. This is made possible thanks to the introduction of novel time-dependent features that capture Ponzi behaviours characteristics derived from our comprehensive data analyses on Ponzi and non-Ponzi data from the XBlock-ETH repository

The vulnerabilities to backdoor attacks have recently threatened the trustworthiness of machine learning models in practical applications. Conventional wisdom suggests that not everyone can be an attacker since the process of designing the trigger generation algorithm often involves significant effort and extensive experimentation to ensure the attack's stealthiness and effectiveness. Alternatively, this paper shows that there exists a more severe backdoor threat: anyone can exploit an easily-accessible algorithm for silent backdoor attacks. Specifically, this attacker can employ the widely-used lossy image compression from a plethora of compression tools to effortlessly inject a trigger pattern into an image without leaving any noticeable trace; i.e., the generated triggers are natural artifacts. One does not require extensive knowledge to click on the "convert" or "save as" button while using tools for lossy image compression. Via this attack, the adversary does not need to design a trigger generator as seen in prior works and only requires poisoning the data. Empirically, the proposed attack consistently achieves 100% attack success rate in several benchmark datasets such as MNIST, CIFAR-10, GTSRB and CelebA. More significantly, the proposed attack can still achieve almost 100% attack success rate with very small (approximately 10%) poisoning rates in the clean label setting. The generated trigger of the proposed attack using one lossy compression algorithm is also transferable across other related compression algorithms, exacerbating the severity of this backdoor threat. This work takes another crucial step toward understanding the extensive risks of backdoor attacks in practice, urging practitioners to investigate similar attacks and relevant backdoor mitigation methods.

Decentralized federated learning (DFL) has gained popularity due to its practicality across various applications. Compared to the centralized version, training a shared model among a large number of nodes in DFL is more challenging, as there is no central server to coordinate the training process. Especially when distributed nodes suffer from limitations in communication or computational resources, DFL will experience extremely inefficient and unstable training. Motivated by these challenges, in this paper, we develop a novel algorithm based on the framework of the inexact alternating direction method (iADM). On one hand, our goal is to train a shared model with a sparsity constraint. This constraint enables us to leverage one-bit compressive sensing (1BCS), allowing transmission of one-bit information among neighbour nodes. On the other hand, communication between neighbour nodes occurs only at certain steps, reducing the number of communication rounds. Therefore, the algorithm exhibits notable communication efficiency. Additionally, as each node selects only a subset of neighbours to participate in the training, the algorithm is robust against stragglers. Additionally, complex items are computed only once for several consecutive steps and subproblems are solved inexactly using closed-form solutions, resulting in high computational efficiency. Finally, numerical experiments showcase the algorithm's effectiveness in both communication and computation.

Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other supervised methods and remain one of the most popular approaches in the machine learning arena. In this work, we investigate the training of SVMs through a smooth sparse-promoting-regularized squared hinge loss minimization. This choice paves the way to the application of quick training methods built on majorization-minimization approaches, benefiting from the Lipschitz differentiabililty of the loss function. Moreover, the proposed approach allows us to handle sparsity-preserving regularizers promoting the selection of the most significant features, so enhancing the performance. Numerical tests and comparisons conducted on three different datasets demonstrate the good performance of the proposed methodology in terms of qualitative metrics (accuracy, precision, recall, and F 1 score) as well as computational cost.

Despite recent advancements in image generation, diffusion models still remain largely underexplored in Earth Observation. In this paper we show that state-of-the-art pretrained diffusion models can be conditioned on cartographic data to generate realistic satellite images. We provide two large datasets of paired OpenStreetMap images and satellite views over the region of Mainland Scotland and the Central Belt. We train a ControlNet model and qualitatively evaluate the results, demonstrating that both image quality and map fidelity are possible. Finally, we provide some insights on the opportunities and challenges of applying these models for remote sensing. Our model weights and code for creating the dataset are publicly available at https://github.com/miquel-espinosa/map-sat.

Artificial Intelligence (AI) is often an integral part of modern decision support systems (DSSs). The best-performing predictive models used in AI-based DSSs lack transparency. Explainable Artificial Intelligence (XAI) aims to create AI systems that can explain their rationale to human users. Local explanations in XAI can provide information about the causes of individual predictions in terms of feature importance. However, a critical drawback of existing local explanation methods is their inability to quantify the uncertainty associated with a feature's importance. This paper introduces an extension of a feature importance explanation method, Calibrated Explanations (CE), previously only supporting classification, with support for standard regression and probabilistic regression, i.e., the probability that the target is above an arbitrary threshold. The extension for regression keeps all the benefits of CE, such as calibration of the prediction from the underlying model with confidence intervals, uncertainty quantification of feature importance, and allows both factual and counterfactual explanations. CE for standard regression provides fast, reliable, stable, and robust explanations. CE for probabilistic regression provides an entirely new way of creating probabilistic explanations from any ordinary regression model and with a dynamic selection of thresholds. The performance of CE for probabilistic regression regarding stability and speed is comparable to LIME. The method is model agnostic with easily understood conditional rules. An implementation in Python is freely available on GitHub and for installation using pip making the results in this paper easily replicable.

We introduce and study the theory of training neural networks using interpolation techniques from reproducing kernel Hilbert space theory. We generalize the method to Krein spaces, and show that widely-used neural network architectures are subsets of reproducing kernel Krein spaces (RKKS). We study the concept of "associated Hilbert spaces" of RKKS and develop techniques to improve upon the expressivity of various activation functions. Next, using concepts from the theory of functions of several complex variables, we prove a computationally applicable, multidimensional generalization of the celebrated Adamjan- Arov-Krein (AAK) theorem. The theorem yields a novel class of neural networks, called Prolongation Neural Networks (PNN). We demonstrate that, by applying the multidimensional AAK theorem to gain a PNN, one can gain performance superior to both our interpolatory methods and current state-of-the-art methods in noisy environments. We provide useful illustrations of our methods in practice.

Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. Methods In this multinational retrospective observational study we enrolled adult participants (aged $\ge$18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year followup after Roux-en-Y gastric bypass, sleeve g…

Detection of tumors in metastatic colorectal cancer (mCRC) plays an essential role in the early diagnosis and treatment of liver cancer. Deep learning models backboned by fully convolutional neural networks (FCNNs) have become the dominant model for segmenting 3D computerized tomography (CT) scans. However, since their convolution layers suffer from limited kernel size, they are not able to capture long-range dependencies and global context. To tackle this restriction, vision transformers have been introduced to solve FCNN's locality of receptive fields. Although transformers can capture long-range features, their segmentation performance decreases with various tumor sizes due to the model sensitivity to the input patch size. While finding an optimal patch size improves the performance of vision transformer-based models on segmentation tasks, it is a time-consuming and challenging procedure. This paper proposes a technique to select the vision transformer's optimal input multi-resolution image patch size based on the average volume size of metastasis lesions. We further validated our suggested framework using a transfer-learning technique, demonstrating that the highest Dice similarity coefficient (DSC) performance was obtained by pre-training on training data with a larger tumour volume using the suggested ideal patch size and then training with a smaller one. We experimentally evaluate this idea through pre-training our model on a multi-resolution public dataset. Our model showed consistent and improved results when applied to our private multi-resolution mCRC dataset with a smaller average tumor volume. This study lays the groundwork for optimizing semantic segmentation of small objects using vision transformers. The implementation source code is available at:https://github.com/Ramtin-Mojtahedi/OVTPS.

Mobile devices have widespread to become the most used piece of technology. Due to their characteristics, they have become major targets for botnet-related malware. FluBot is one example of botnet malware that infects mobile devices. In particular, FluBot is a DNS-based botnet that uses Domain Generation Algorithms (DGA) to establish communication with the Command and Control Server (C2). MONDEO is a multistage mechanism with a flexible design to detect DNS-based botnet malware. MONDEO is lightweight and can be deployed without requiring the deployment of software, agents, or configuration in mobile devices, allowing easy integration in core networks. MONDEO comprises four detection stages: Blacklisting/Whitelisting, Query rate analysis, DGA analysis, and Machine learning evaluation. It was created with the goal of processing streams of packets to identify attacks with high efficiency, in the distinct phases. MONDEO was tested against several datasets to measure its efficiency and performance, being able to achieve high performance with RandomForest classifiers. The implementation is available at github.

Latent diffusers revolutionized the generative AI and inspired creative art. When denoising the latent, the predicted original image at each step collectively animates the formation. However, the animation is limited by the denoising nature of the diffuser, and only renders a sharpening process. This work presents Latent Painter, which uses the latent as the canvas, and the diffuser predictions as the plan, to generate painting animation. Latent Painter also transits one generated image to another, which can happen between images from two different sets of checkpoints.

Radio map construction requires a large amount of radio measurement data with location labels, which imposes a high deployment cost. This paper develops a region-based radio map from received signal strength (RSS) measurements without location labels. The construction is based on a set of blindly collected RSS measurement data from a device that visits each region in an indoor area exactly once, where the footprints and timestamps are not recorded. The main challenge is to cluster the RSS data and match clusters with the physical regions. Classical clustering algorithms fail to work as the RSS data naturally appears as non-clustered due to multipaths and noise. In this paper, a signal subspace model with a sequential prior is constructed for the RSS data, and an integrated segmentation and clustering algorithm is developed, which is shown to find the globally optimal solution in a special case. Furthermore, the clustered data is matched with the physical regions using a graph-based approach. Based on real measurements from an office space, the proposed scheme reduces the region localization error by roughly 50% compared to a weighted centroid localization (WCL) baseline, and it even outperforms some supervised localization schemes, including k-nearest neighbor (KNN), support vector machine (SVM), and deep neural network (DNN), which require labeled data for training.

The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples are usually partially available due to data corruption or sensor malfunction, which raises the research of incomplete multi-view clustering (IMVC). Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID). Such the AUP-ID would cause inaccurate graph fusion and degrades clustering performance. To tackle these issues, we propose a novel incomplete anchor graph learning framework termed Scalable Incomplete Multi-View Clustering with Structure Alignment (SIMVC-SA). Specially, we construct the view-specific anchor graph to capture the complementary information from different views. In order to solve the AUP-ID, we propose a novel structure alignment module to refine the cross-view anchor correspondence. Meanwhile, the anchor graph construction and alignment are jointly optimized in our unified framework to enhance clustering quality. Through anchor graph construction instead of full graphs, the time and space complexity of the proposed SIMVC-SA is proven to be linearly correlated with the number of samples. Extensive experiments on seven incomplete benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Our code is publicly available at https://github.com/wy1019/SIMVC-SA.

Masked image modeling has been demonstrated as a powerful pretext task for generating robust representations that can be effectively generalized across multiple downstream tasks. Typically, this approach involves randomly masking patches (tokens) in input images, with the masking strategy remaining unchanged during training. In this paper, we propose a curriculum learning approach that updates the masking strategy to continually increase the complexity of the self-supervised reconstruction task. We conjecture that, by gradually increasing the task complexity, the model can learn more sophisticated and transferable representations. To facilitate this, we introduce a novel learnable masking module that possesses the capability to generate masks of different complexities, and integrate the proposed module into masked autoencoders (MAE). Our module is jointly trained with the MAE, while adjusting its behavior during training, transitioning from a partner to the MAE (optimizing the same reconstruction loss) to an adversary (optimizing the opposite loss), while passing through a neutral state. The transition between these behaviors is smooth, being regulated by a factor that is multiplied with the reconstruction loss of the masking module. The resulting training procedure generates an easy-to-hard curriculum. We train our Curriculum-Learned Masked Autoencoder (CL-MAE) on ImageNet and show that it exhibits superior representation learning capabilities compared to MAE. The empirical results on five downstream tasks confirm our conjecture, demonstrating that curriculum learning can be successfully used to self-supervise masked autoencoders.

We can learn from analyzing quantum convolutional neural networks (QCNNs) that: 1) working with quantum data can be perceived as embedding physical system parameters through a hidden feature map; 2) their high performance for quantum phase recognition can be attributed to generation of a very suitable basis set during the ground state embedding, where quantum criticality of spin models leads to basis functions with rapidly changing features; 3) pooling layers of QCNNs are responsible for picking those basis functions that can contribute to forming a high-performing decision boundary, and the learning process corresponds to adapting the measurement such that few-qubit operators are mapped to full-register observables; 4) generalization of QCNN models strongly depends on the embedding type, and that rotation-based feature maps with the Fourier basis require careful feature engineering; 5) accuracy and generalization of QCNNs with readout based on a limited number of shots favor the ground state embeddings and associated physics-informed models. We demonstrate these points in simulation, where our results shed light on classification for physical processes, relevant for applications in sensing. Finally, we show that QCNNs with properly chosen ground state embeddings can be used for fluid dynamics problems, expressing shock wave solutions with good generalization and proven trainability.

In the rapidly evolving digital era, the analysis of document layouts plays a pivotal role in automated information extraction and interpretation. In our work, we have trained MViTv2 transformer model architecture with cascaded mask R-CNN on BaDLAD dataset to extract text box, paragraphs, images and tables from a document. After training on 20365 document images for 36 epochs in a 3 phase cycle, we achieved a training loss of 0.2125 and a mask loss of 0.19. Our work extends beyond training, delving into the exploration of potential enhancement avenues. We investigate the impact of rotation and flip augmentation, the effectiveness of slicing input images pre-inference, the implications of varying the resolution of the transformer backbone, and the potential of employing a dual-pass inference to uncover missed text-boxes. Through these explorations, we observe a spectrum of outcomes, where some modifications result in tangible performance improvements, while others offer unique insights for future endeavors.

Pre-trained language models like ChatGPT have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate tasks. Moreover, in bioinformatics, generating functional programs poses additional notable challenges due to the amount of domain knowledge, the need for complicated data operations, and intricate functional dependencies between the operations. Here, we present BioCoder, a benchmark developed to evaluate existing pre-trained models in generating bioinformatics code. In relation to function-code generation, BioCoder covers potential package dependencies, class declarations, and global variables. It incorporates 1026 functions and 1243 methods in Python and Java from GitHub and 253 examples from the Rosalind Project. BioCoder incorporates a fuzz-testing framework for evaluation, and we have applied it to evaluate many models including InCoder, CodeGen, CodeGen2, SantaCoder, StarCoder, StarCoder+, InstructCodeT5+, and ChatGPT. Our detailed analysis of these models emphasizes the importance of domain knowledge, pragmatic code generation, and contextual understanding. Our dataset, benchmark, Docker images, and scripts required for testing are all available at https://github.com/gersteinlab/biocoder.

Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution. In fact, most real-world graph data naturally presents a long-tailed form, where the head classes occupy much more samples than the tail classes, it thus is essential to study the graph-level classification over long-tailed data while still remaining largely unexplored. However, most existing long-tailed learning methods in visions fail to jointly optimize the representation learning and classifier training, as well as neglect the mining of the hard-to-classify classes. Directly applying existing methods to graphs may lead to sub-optimal performance, since the model trained on graphs would be more sensitive to the long-tailed distribution due to the complex topological characteristics. Hence, in this paper, we propose a novel long-tailed graph-level classification framework via Collaborative Multi-expert Learning (CoMe) to tackle the problem. To equilibrate the contributions of head and tail classes, we first develop balanced contrastive learning from the view of representation learning, and then design an individual-expert classifier training based on hard class mining. In addition, we execute gated fusion and disentangled knowledge distillation among the multiple experts to promote the collaboration in a multi-expert framework. Comprehensive experiments are performed on seven widely-used benchmark datasets to demonstrate the superiority of our method CoMe over state-of-the-art baselines.

The spontaneous behavior that often occurs in conversations makes speech more human-like compared to reading-style. However, synthesizing spontaneous-style speech is challenging due to the lack of high-quality spontaneous datasets and the high cost of labeling spontaneous behavior. In this paper, we propose a semi-supervised pre-training method to increase the amount of spontaneous-style speech and spontaneous behavioral labels. In the process of semi-supervised learning, both text and speech information are considered for detecting spontaneous behaviors labels in speech. Moreover, a linguistic-aware encoder is used to model the relationship between each sentence in the conversation. Experimental results indicate that our proposed method achieves superior expressive speech synthesis performance with the ability to model spontaneous behavior in spontaneous-style speech and predict reasonable spontaneous behavior from text.

To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects. Most existing approaches have difficulties to extend predictions to scenarios where novel object instances are continuously introduced, especially with heavy occlusions. In this work, we propose a few-shot pose estimation (FSPE) approach called SA6D, which uses a self-adaptive segmentation module to identify the novel target object and construct a point cloud model of the target object using only a small number of cluttered reference images. Unlike existing methods, SA6D does not require object-centric reference images or any additional object information, making it a more generalizable and scalable solution across categories. We evaluate SA6D on real-world tabletop object datasets and demonstrate that SA6D outperforms existing FSPE methods, particularly in cluttered scenes with occlusions, while requiring fewer reference images.

A lot of deep learning (DL) research these days is mainly focused on improving quantitative metrics regardless of other factors. In human-centered applications, like skin lesion classification in dermatology, DL-driven clinical decision support systems are still in their infancy due to the limited transparency of their decision-making process. Moreover, the lack of procedures that can explain the behavior of trained DL algorithms leads to almost no trust from clinical physicians. To diagnose skin lesions, dermatologists rely on visual assessment of the disease and the data gathered from the patient's anamnesis. Data-driven algorithms dealing with multi-modal data are limited by the separation of feature-level and decision-level fusion procedures required by convolutional architectures. To address this issue, we enable single-stage multi-modal data fusion via the attention mechanism of transformer-based architectures to aid in diagnosing skin diseases. Our method beats other state-of-the-art single- and multi-modal DL architectures in image-rich and patient-data-rich environments. Additionally, the choice of the architecture enables native interpretability support for the classification task both in the image and metadata domain with no additional modifications necessary.

Game theory offers an interpretable mathematical framework for modeling multi-agent interactions. However, its applicability in real-world robotics applications is hindered by several challenges, such as unknown agents' preferences and goals. To address these challenges, we show a connection between differential games, optimal control, and energy-based models and demonstrate how existing approaches can be unified under our proposed Energy-based Potential Game formulation. Building upon this formulation, this work introduces a new end-to-end learning application that combines neural networks for game-parameter inference with a differentiable game-theoretic optimization layer, acting as an inductive bias. The experiments using simulated mobile robot pedestrian interactions and real-world automated driving data provide empirical evidence that the game-theoretic layer improves the predictive performance of various neural network backbones.

Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently. To address CNNC, we propose a domain-adaptive message passing graph neural network (DM-GNN), which integrates graph neural network (GNN) with conditional adversarial domain adaptation. DM-GNN is capable of learning informative representations for node classification that are also transferrable across networks. Firstly, a GNN encoder is constructed by dual feature extractors to separate ego-embedding learning from neighbor-embedding learning so as to jointly capture commonality and discrimination between connected nodes. Secondly, a label propagation node classifier is proposed to refine each node's label prediction by combining its own prediction and its neighbors' prediction. In addition, a label-aware propagation scheme is devised for the labeled source network to promote intra-class propagation while avoiding inter-class propagation, thus yielding label-discriminative source embeddings. Thirdly, conditional adversarial domain adaptation is performed to take the neighborhood-refined class-label information into account during adversarial domain adaptation, so that the class-conditional distributions across networks can be better matched. Comparisons with eleven state-of-the-art methods demonstrate the effectiveness of the proposed DM-GNN.

We present Point-TTA, a novel test-time adaptation framework for point cloud registration (PCR) that improves the generalization and the performance of registration models. While learning-based approaches have achieved impressive progress, generalization to unknown testing environments remains a major challenge due to the variations in 3D scans. Existing methods typically train a generic model and the same trained model is applied on each instance during testing. This could be sub-optimal since it is difficult for the same model to handle all the variations during testing. In this paper, we propose a test-time adaptation approach for PCR. Our model can adapt to unseen distributions at test-time without requiring any prior knowledge of the test data. Concretely, we design three self-supervised auxiliary tasks that are optimized jointly with the primary PCR task. Given a test instance, we adapt our model using these auxiliary tasks and the updated model is used to perform the inference. During training, our model is trained using a meta-auxiliary learning approach, such that the adapted model via auxiliary tasks improves the accuracy of the primary task. Experimental results demonstrate the effectiveness of our approach in improving generalization of point cloud registration and outperforming other state-of-the-art approaches.

Affordable 3D scanners often produce sparse and non-uniform point clouds that negatively impact downstream applications in robotic systems. While existing point cloud upsampling architectures have demonstrated promising results on standard benchmarks, they tend to experience significant performance drops when the test data have different distributions from the training data. To address this issue, this paper proposes a test-time adaption approach to enhance model generality of point cloud upsampling. The proposed approach leverages meta-learning to explicitly learn network parameters for test-time adaption. Our method does not require any prior information about the test data. During meta-training, the model parameters are learned from a collection of instance-level tasks, each of which consists of a sparse-dense pair of point clouds from the training data. During meta-testing, the trained model is fine-tuned with a few gradient updates to produce a unique set of network parameters for each test instance. The updated model is then used for the final prediction. Our framework is generic and can be applied in a plug-and-play manner with existing backbone networks in point cloud upsampling. Extensive experiments demonstrate that our approach improves the performance of state-of-the-art models.

To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGNNs, the previous TGNN evaluations reveal several limitations regarding four critical issues: 1) inconsistent datasets, 2) inconsistent evaluation pipelines, 3) lacking workload diversity, and 4) lacking efficient comparison. Overall, there lacks an empirical study that puts TGNN models onto the same ground and compares them comprehensively. To this end, we propose BenchTemp, a general benchmark for evaluating TGNN models on various workloads. BenchTemp provides a set of benchmark datasets so that different TGNN models can be fairly compared. Further, BenchTemp engineers a standard pipeline that unifies the TGNN evaluation. With BenchTemp, we extensively compare the representative TGNN models on different tasks (e.g., link prediction and node classification) and settings (transductive and inductive), w.r.t. both effectiveness and efficiency metrics. We have made BenchTemp publicly available at https://github.com/qianghuangwhu/benchtemp.

Science is facing a reproducibility crisis. Previous work has proposed incorporating data analysis replications into classrooms as a potential solution. However, despite the potential benefits, it is unclear whether this approach is feasible, and if so, what the involved stakeholders-students, educators, and scientists-should expect from it. Can students perform a data analysis replication over the course of a class? What are the costs and benefits for educators? And how can this solution help benchmark and improve the state of science? In the present study, we incorporated data analysis replications in the project component of the Applied Data Analysis course (CS-401) taught at EPFL (N=354 students). Here we report pre-registered findings based on surveys administered throughout the course. First, we demonstrate that students can replicate previously published scientific papers, most of them qualitatively and some exactly. We find discrepancies between what students expect of data analysis replications and what they experience by doing them along with changes in expectations about reproducibility, which together serve as evidence of attitude shifts to foster students' critical thinking. Second, we provide information for educators about how much overhead is needed to incorporate replications into the classroom and identify concerns that replications bring as compared to more traditional assignments. Third, we identify tangible benefits of the in-class data analysis replications for scientific communities, such as a collection of replication reports and insights about replication barriers in scientific work that should be avoided going forward. Overall, we demonstrate that incorporating replication tasks into a large data science class can increase the reproducibility of scientific work as a by-product of data science instruction, thus benefiting both science and students.

We present a linear algebra formulation of backpropagation which allows the calculation of gradients by using a generically written ``backslash'' or Gaussian elimination on triangular systems of equations. Generally, the matrix elements are operators. This paper has three contributions: (i) it is of intellectual value to replace traditional treatments of automatic differentiation with a (left acting) operator theoretic, graph-based approach; (ii) operators can be readily placed in matrices in software in programming languages such as Julia as an implementation option; (iii) we introduce a novel notation, ``transpose dot'' operator ``$\{\}^{T_\bullet}$'' that allows for the reversal of operators. We further demonstrate the elegance of the operators approach in a suitable programming language consisting of generic linear algebra operators such as Julia \cite{bezanson2017julia}, and that it is possible to realize this abstraction in code. Our implementation shows how generic linear algebra can allow operators as elements of matrices. In contrast to ``operator overloading,'' where backslash would normally have to be rewritten to take advantage of operators, with ``generic programming'' there is no such need.

Graph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web. However, while being rich in information, graphs are often noisy and incomplete. As a result, graph completion tasks, such as node classification or link prediction, have gained attention. On one hand, neural methods, such as graph neural networks, have proven to be robust tools for learning rich representations of noisy graphs. On the other hand, symbolic methods enable exact reasoning on graphs.We propose Knowledge Enhanced Graph Neural Networks (KeGNN), a neuro-symbolic framework for graph completion that combines both paradigms as it allows for the integration of prior knowledge into a graph neural network model.Essentially, KeGNN consists of a graph neural network as a base upon which knowledge enhancement layers are stacked with the goal of refining predictions with respect to prior knowledge.We instantiate KeGNN in conjunction with two state-of-the-art graph neural networks, Graph Convolutional Networks and Graph Attention Networks, and evaluate KeGNN on multiple benchmark datasets for node classification.

Normalizing flow is a class of deep generative models for efficient sampling and density estimation. In practice, the flow often appears as a chain of invertible neural network blocks; to facilitate training, existing works have regularized flow trajectories and designed special network architectures. The current paper develops a neural ODE flow network inspired by the Jordan-Kinderleherer-Otto (JKO) scheme, which allows efficient block-wise training of the residual blocks without sampling SDE trajectories or inner loops of score matching or variational learning. As the JKO scheme unfolds the dynamic of gradient flow, the proposed model naturally stacks residual network blocks one by one, reducing the memory load and difficulty in performing end-to-end deep flow network training. We also develop adaptive time reparameterization of the flow network with a progressive refinement of the trajectory in probability space, which improves the model training efficiency and accuracy in practice. Using numerical experiments with synthetic and real data, we show that the proposed JKO-iFlow model achieves similar or better performance in generating new samples compared with the existing flow and diffusion models at a significantly reduced computational and memory cost.

Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently outperform their homogeneous counterparts on image classification and nonlinear regression tasks. Sub-networks instantiate the neurons, which meta-learn especially efficient sets of nonlinear responses. Examples include conventional neural networks classifying digits and forecasting a van der Pol oscillator and physics-informed Hamiltonian neural networks learning H\'enon-Heiles stellar orbits and the swing of a video recorded pendulum clock. Such \textit{learned diversity} provides examples of dynamical systems selecting diversity over uniformity and elucidates the role of diversity in natural and artificial systems.

Artificial neural networks are being proposed as models of parts of the brain. The networks are compared to recordings of biological neurons, and good performance in reproducing neural responses is considered to support the model's validity. A key question is how much this system identification approach tells us about brain computation. Does it validate one model architecture over another? We evaluate the most commonly used comparison techniques, such as a linear encoding model and centered kernel alignment, to correctly identify a model by replacing brain recordings with known ground truth models. System identification performance is quite variable; it also depends significantly on factors independent of the ground truth architecture, such as stimuli images. In addition, we show the limitations of using functional similarity scores in identifying higher-level architectural motifs.

We present a method for balancing between the Local and Global Structures (LGS) in graph embedding, via a tunable parameter. Some embedding methods aim to capture global structures, while others attempt to preserve local neighborhoods. Few methods attempt to do both, and it is not always possible to capture well both local and global information in two dimensions, which is where most graph drawing live. The choice of using a local or a global embedding for visualization depends not only on the task but also on the structure of the underlying data, which may not be known in advance. For a given graph, LGS aims to find a good balance between the local and global structure to preserve. We evaluate the performance of LGS with synthetic and real-world datasets and our results indicate that it is competitive with the state-of-the-art methods, using established quality metrics such as stress and neighborhood preservation. We introduce a novel quality metric, cluster distance preservation, to assess intermediate structure capture. All source-code, datasets, experiments and analysis are available online.

Mobile Internet has profoundly reshaped modern lifestyles in various aspects. Encrypted Traffic Classification (ETC) naturally plays a crucial role in managing mobile Internet, especially with the explosive growth of mobile apps using encrypted communication. Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic. None of the existing ETC methods can address all these limitations. In this paper, we propose a novel Pre-trAining Semi-Supervised ETC framework, dubbed PASS. Our key insight is to resample the original train dataset and perform contrastive pre-training without using individual app labels directly to avoid label bias issues caused by class imbalance, while obtaining a robust feature representation to differentiate overlapping homogeneous traffic by pulling positive traffic pairs closer and pushing negative pairs away. Meanwhile, PASS designs a semi-supervised optimization strategy based on pseudo-label iteration and dynamic loss weighting algorithms in order to effectively utilize massive unlabeled traffic data and alleviate manual train dataset annotation workload. PASS outperforms state-of-the-art ETC methods and generic sampling approaches on four public datasets with significant class imbalance and traffic homogeneity, remarkably pushing the F1 of Cross-Platform215 with 1.31%, ISCX-17 with 9.12%. Furthermore, we validate the generality of the contrastive pre-training and pseudo-label iteration components of PASS, which can adaptively benefit ETC methods with diverse feature extractors.

In this paper, we propose a new way of remembering by introducing a memory influence mechanism for the least squares support vector machine (LSSVM). Without changing the equation constraints of the original LSSVM, this mechanism, allows an accurate partitioning of the training set without overfitting. The maximum memory impact model (MIMM) and the weighted impact memory model (WIMM) are then proposed. It is demonstrated that these models can be degraded to the LSSVM. Furthermore, we propose some different memory impact functions for the MIMM and WIMM. The experimental results show that that our MIMM and WIMM have better generalization performance compared to the LSSVM and significant advantage in time cost compared to other memory models.

We present a new nonlinear variational framework for simultaneously computing ground and excited states of quantum systems. Our approach is based on approximating wavefunctions in the linear span of basis functions that are augmented and optimized \emph{via} composition with normalizing flows. The accuracy and efficiency of our approach are demonstrated in the calculations of a large number of vibrational states of the triatomic H$_2$S molecule as well as ground and several excited electronic states of prototypical one-electron systems including the hydrogen atom, the molecular hydrogen ion, and a carbon atom in a single-active-electron approximation. The results demonstrate significant improvements in the accuracy of energy predictions and accelerated basis-set convergence even when using normalizing flows with a small number of parameters. The present approach can be also seen as the optimization of a set of intrinsic coordinates that best capture the underlying physics within the given basis set.

Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling. We extend this paradigm by utilizing LLMs for material property prediction by introducing our model Materials Informatics Transformer (MatInFormer). Specifically, we introduce a novel approach that involves learning the grammar of crystallography through the tokenization of pertinent space group information. We further illustrate the adaptability of MatInFormer by incorporating task-specific data pertaining to Metal-Organic Frameworks (MOFs). Through attention visualization, we uncover the key features that the model prioritizes during property prediction. The effectiveness of our proposed model is empirically validated across 14 distinct datasets, hereby underscoring its potential for high throughput screening through accurate material property prediction.

Implicit neural networks have demonstrated remarkable success in various tasks. However, there is a lack of theoretical analysis of the connections and differences between implicit and explicit networks. In this paper, we study high-dimensional implicit neural networks and provide the high dimensional equivalents for the corresponding conjugate kernels and neural tangent kernels. Built upon this, we establish the equivalence between implicit and explicit networks in high dimensions.

We present ToddlerBERTa, a BabyBERTa-like language model, exploring its capabilities through five different models with varied hyperparameters. Evaluating on BLiMP, SuperGLUE, MSGS, and a Supplement benchmark from the BabyLM challenge, we find that smaller models can excel in specific tasks, while larger models perform well with substantial data. Despite training on a smaller dataset, ToddlerBERTa demonstrates commendable performance, rivalling the state-of-the-art RoBERTa-base. The model showcases robust language understanding, even with single-sentence pretraining, and competes with baselines that leverage broader contextual information. Our work provides insights into hyperparameter choices, and data utilization, contributing to the advancement of language models.

In this paper we propose a proximal subgradient method (Prox-SubGrad) for solving nonconvex and nonsmooth optimization problems without assuming Lipschitz continuity conditions. A number of subgradient upper bounds and their relationships are presented. By means of these upper bounding conditions, we establish some uniform recursive relations for the Moreau envelopes for weakly convex optimization. This uniform scheme simplifies and unifies the proof schemes to establish rate of convergence for Prox-SubGrad without assuming Lipschitz continuity. We present a novel convergence analysis in this context. Furthermore, we propose some new stochastic subgradient upper bounding conditions and establish convergence and iteration complexity rates for the stochastic subgradient method (Sto-SubGrad) to solve non-Lipschitz and nonsmooth stochastic optimization problems. In particular, for both deterministic and stochastic subgradient methods on weakly convex optimization problems without Lipschitz continuity, under any of the subgradient upper bounding conditions to be introduced in the paper, we show that $O(1/\sqrt{T})$ convergence rate holds in terms of the square of gradient of the Moreau envelope function, which further improves to be $O(1/{T})$ if, in addition, the uniform KL condition with exponent $1/2$ holds.

This paper addresses the tradeoff between standard accuracy on clean examples and robustness against adversarial examples in deep neural networks (DNNs). Although adversarial training (AT) improves robustness, it degrades the standard accuracy, thus yielding the tradeoff. To mitigate this tradeoff, we propose a novel AT method called ARREST, which comprises three components: (i) adversarial finetuning (AFT), (ii) representation-guided knowledge distillation (RGKD), and (iii) noisy replay (NR). AFT trains a DNN on adversarial examples by initializing its parameters with a DNN that is standardly pretrained on clean examples. RGKD and NR respectively entail a regularization term and an algorithm to preserve latent representations of clean examples during AFT. RGKD penalizes the distance between the representations of the standardly pretrained and AFT DNNs. NR switches input adversarial examples to nonadversarial ones when the representation changes significantly during AFT. By combining these components, ARREST achieves both high standard accuracy and robustness. Experimental results demonstrate that ARREST mitigates the tradeoff more effectively than previous AT-based methods do.

Since their inception in 2014, Generative Adversarial Networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas. Consisting of a discriminative network and a generative network engaged in a Minimax game, GANs have revolutionized the field of generative modeling. In February 2018, GAN secured the leading spot on the ``Top Ten Global Breakthrough Technologies List'' issued by the Massachusetts Science and Technology Review. Over the years, numerous advancements have been proposed, leading to a rich array of GAN variants, such as conditional GAN, Wasserstein GAN, CycleGAN, and StyleGAN, among many others. This survey aims to provide a general overview of GANs, summarizing the latent architecture, validation metrics, and application areas of the most widely recognized variants. We also delve into recent theoretical developments, exploring the profound connection between the adversarial principle underlying GAN and Jensen-Shannon divergence, while discussing the optimality characteristics of the GAN framework. The efficiency of GAN variants and their model architectures will be evaluated along with training obstacles as well as training solutions. In addition, a detailed discussion will be provided, examining the integration of GANs with newly developed deep learning frameworks such as Transformers, Physics-Informed Neural Networks, Large Language models, and Diffusion models. Finally, we reveal several issues as well as future research outlines in this field.

Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily suffer from the following limitations. Firstly, users generally click different types of items from multiple scenarios, and modeling from multiple scenarios can provide a more comprehensive understanding of users. Existing datasets only include data for the same type of items from a single scenario. Secondly, multi-modal features are essential in multi-scenario prediction as they address the issue of inconsistent ID encoding between different scenarios. The existing datasets are based on ID features and lack multi-modal features. Third, a large-scale dataset can provide a more reliable evaluation of models, fully reflecting the performance differences between models. The scale of existing datasets is around 100 million, which is relatively small compared to the real-world CTR prediction. To address these limitations, we propose AntM$^{2}$C, a Multi-Scenario Multi-Modal CTR dataset based on industrial data from Alipay. Specifically, AntM$^{2}$C provides the following advantages: 1) It covers CTR data of 5 different types of items, providing insights into the preferences of users for different items, including advertisements, vouchers, mini-programs, contents, and videos. 2) Apart from ID-based features, AntM$^{2}$C also provides 2 multi-modal features, raw text and image features, which can effectively establish connections between items with different IDs. 3) AntM$^{2}$C provides 1 billion CTR data with 200 features, including 200 million users and 6 million items. It is currently the largest-scale CTR dataset available. Based on AntM$^{2}$C, we construct several typical CTR tasks and provide comparisons with baseline methods. The dataset homepage is available at https://www.atecup.cn/home.

Transformer-like models for vision tasks have recently proven effective for a wide range of downstream applications such as segmentation and detection. Previous works have shown that segmentation properties emerge in vision transformers (ViTs) trained using self-supervised methods such as DINO, but not in those trained on supervised classification tasks. In this study, we probe whether segmentation emerges in transformer-based models solely as a result of intricate self-supervised learning mechanisms, or if the same emergence can be achieved under much broader conditions through proper design of the model architecture. Through extensive experimental results, we demonstrate that when employing a white-box transformer-like architecture known as CRATE, whose design explicitly models and pursues low-dimensional structures in the data distribution, segmentation properties, at both the whole and parts levels, already emerge with a minimalistic supervised training recipe. Layer-wise finer-grained analysis reveals that the emergent properties strongly corroborate the designed mathematical functions of the white-box network. Our results suggest a path to design white-box foundation models that are simultaneously highly performant and mathematically fully interpretable. Code is at \url{https://github.com/Ma-Lab-Berkeley/CRATE}.

Emotion recognition using electroencephalogram (EEG) mainly has two scenarios: classification of the discrete labels and regression of the continuously tagged labels. Although many algorithms were proposed for classification tasks, there are only a few methods for regression tasks. For emotion regression, the label is continuous in time. A natural method is to learn the temporal dynamic patterns. In previous studies, long short-term memory (LSTM) and temporal convolutional neural networks (TCN) were utilized to learn the temporal contextual information from feature vectors of EEG. However, the spatial patterns of EEG were not effectively extracted. To enable the spatial learning ability of TCN towards better regression and classification performances, we propose a novel unified model, named MASA-TCN, for EEG emotion regression and classification tasks. The space-aware temporal layer enables TCN to additionally learn from spatial relations among EEG electrodes. Besides, a novel multi-anchor block with attentive fusion is proposed to learn dynamic temporal dependencies. Experiments on two publicly available datasets show MASA-TCN achieves higher results than the state-of-the-art methods for both EEG emotion regression and classification tasks. The code is available at https://github.com/yi-ding-cs/MASA-TCN.

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as accuracy, with a lack of privacy consideration, which is a major concern in modern society where privacy attacks are rampant. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain. In this survey, we aim to address this gap by summarizing the attacks on graph data according to the targeted information, categorizing the privacy preservation techniques in GNNs, and reviewing the datasets and applications that could be used for analyzing/solving privacy issues in GNNs. We also outline potential directions for future research in order to build better privacy-preserving GNNs.

Performance of classifiers is often measured in terms of average accuracy on test data. Despite being a standard measure, average accuracy fails in characterizing the fit of the model to the underlying conditional law of labels given the features vector ($Y|X$), e.g. due to model misspecification, over fitting, and high-dimensionality. In this paper, we consider the fundamental problem of assessing the goodness-of-fit for a general binary classifier. Our framework does not make any parametric assumption on the conditional law $Y|X$, and treats that as a black box oracle model which can be accessed only through queries. We formulate the goodness-of-fit assessment problem as a tolerance hypothesis testing of the form \[ H_0: \mathbb{E}\Big[D_f\Big({\sf Bern}(\eta(X))\|{\sf Bern}(\hat{\eta}(X))\Big)\Big]\leq \tau\,, \] where $D_f$ represents an $f$-divergence function, and $\eta(x)$, $\hat{\eta}(x)$ respectively denote the true and an estimate likelihood for a feature vector $x$ admitting a positive label. We propose a novel test, called \grasp for testing $H_0$, which works in finite sample settings, no matter the features (distribution-free). We also propose model-X \grasp designed for model-X settings where the joint distribution of the features vector is known. Model-X \grasp uses this distributional information to achieve better power. We evaluate the performance of our tests through extensive numerical experiments.

In modern communication systems, efficient and reliable information dissemination is crucial for supporting critical operations across domains like disaster response, autonomous vehicles, and sensor networks. This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach as a significant step forward in achieving more decentralized, efficient, and collaborative solutions. We propose a Decentralized-POMDP formulation for information dissemination, empowering each agent to independently decide on message forwarding. This constitutes a significant paradigm shift from traditional heuristics based on Multi-Point Relay (MPR) selection. Our approach harnesses Graph Convolutional Reinforcement Learning, employing Graph Attention Networks (GAT) with dynamic attention to capture essential network features. We propose two approaches, L-DGN and HL-DGN, which differ in the information that is exchanged among agents. We evaluate the performance of our decentralized approaches, by comparing them with a widely-used MPR heuristic, and we show that our trained policies are able to efficiently cover the network while bypassing the MPR set selection process. Our approach promises a first step toward bolstering the resilience of real-world broadcast communication infrastructures via learned, collaborative information dissemination.

Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing reaction pathways from commercially available starting materials to a target molecule. Each step in multi-step retrosynthesis planning requires accurate prediction of possible precursor molecules given the target molecule and confidence estimates to guide heuristic search algorithms. We model single-step retrosynthesis planning as a distribution learning problem in a discrete state space. First, we introduce the Markov Bridge Model, a generative framework aimed to approximate the dependency between two intractable discrete distributions accessible via a finite sample of coupled data points. Our framework is based on the concept of a Markov bridge, a Markov process pinned at its endpoints. Unlike diffusion-based methods, our Markov Bridge Model does not need a tractable noise distribution as a sampling proxy and directly operates on the input product molecules as samples from the intractable prior distribution. We then address the retrosynthesis planning problem with our novel framework and introduce RetroBridge, a template-free retrosynthesis modeling approach that achieves state-of-the-art results on standard evaluation benchmarks.

Lossy video compression is commonly used when transmitting and storing video data. Unified video codecs (e.g., H.264 or H.265) remain the \emph{de facto} standard, despite the availability of advanced (neural) compression approaches. Transmitting videos in the face of dynamic network bandwidth conditions requires video codecs to adapt to vastly different compression strengths. Rate control modules augment the codec's compression such that bandwidth constraints are satisfied and video distortion is minimized. While, both standard video codes and their rate control modules are developed to minimize video distortion w.r.t. human quality assessment, preserving the downstream performance of deep vision models is not considered. In this paper, we present the first end-to-end learnable deep video codec control considering both bandwidth constraints and downstream vision performance, while not breaking existing standardization. We demonstrate for two common vision tasks (semantic segmentation and optical flow estimation) and on two different datasets that our deep codec control better preserves downstream performance than using 2-pass average bit rate control while meeting dynamic bandwidth constraints and adhering to standardizations.

We consider the fractional elliptic problem with Dirichlet boundary conditions on a bounded and convex domain $D$ of $\mathbb{R}^d$, with $d \geq 2$. In this paper, we perform a stochastic gradient descent algorithm that approximates the solution of the fractional problem via Deep Neural Networks. Additionally, we provide four numerical examples to test the efficiency of the algorithm, and each example will be studied for many values of $\alpha \in (1,2)$ and $d \geq 2$.

Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected graphs. However, transformers for directed graphs are a surprisingly underexplored topic, despite their applicability to ubiquitous domains, including source code and logic circuits. In this work, we propose two direction- and structure-aware positional encodings for directed graphs: (1) the eigenvectors of the Magnetic Laplacian - a direction-aware generalization of the combinatorial Laplacian; (2) directional random walk encodings. Empirically, we show that the extra directionality information is useful in various downstream tasks, including correctness testing of sorting networks and source code understanding. Together with a data-flow-centric graph construction, our model outperforms the prior state of the art on the Open Graph Benchmark Code2 relatively by 14.7%.

Large-scale linear systems, $Ax=b$, frequently arise in practice and demand effective iterative solvers. Often, these systems are noisy due to operational errors or faulty data-collection processes. In the past decade, the randomized Kaczmarz (RK) algorithm has been studied extensively as an efficient iterative solver for such systems. However, the convergence study of RK in the noisy regime is limited and considers measurement noise in the right-hand side vector, $b$. Unfortunately, in practice, that is not always the case; the coefficient matrix $A$ can also be noisy. In this paper, we analyze the convergence of RK for noisy linear systems when the coefficient matrix, $A$, is corrupted with both additive and multiplicative noise, along with the noisy vector, $b$. In our analyses, the quantity $\tilde R=\| \tilde A^{\dagger} \|_2^2 \|\tilde A \|_F^2$ influences the convergence of RK, where $\tilde A$ represents a noisy version of $A$. We claim that our analysis is robust and realistically applicable, as we do not require information about the noiseless coefficient matrix, $A$, and considering different conditions on noise, we can control the convergence of RK. We substantiate our theoretical findings by performing comprehensive numerical experiments.

Graph Neural Networks (GNNs) have emerged as a prominent research topic in the field of machine learning. Existing GNN models are commonly categorized into two types: spectral GNNs, which are designed based on polynomial graph filters, and spatial GNNs, which utilize a message-passing scheme as the foundation of the model. For the expressive power and universality of spectral GNNs, a natural approach is to improve the design of basis functions for better approximation ability. As for spatial GNNs, models like Graph Isomorphism Networks (GIN) analyze their expressive power based on Graph Isomorphism Tests. Recently, there have been attempts to establish connections between spatial GNNs and geometric concepts like curvature and cellular sheaves, as well as physical phenomena like oscillators. However, despite the recent progress, there is still a lack of comprehensive analysis regarding the universality of spatial GNNs from the perspectives of geometry and physics. In this paper, we propose MetricGNN (MGNN), a spatial GNN model inspired by the congruent-insensitivity property of classifiers in the classification phase of GNNs. We demonstrate that a GNN model is universal in the spatial domain if it can generate embedding matrices that are congruent to any given embedding matrix. This property is closely related to the Distance Geometry Problem (DGP). Since DGP is an NP-Hard combinatorial optimization problem, we propose optimizing an energy function derived from spring networks and the Multi-Dimensional Scaling (MDS) problem. This approach also allows our model to handle both homophilic and heterophilic graphs. Finally, we propose employing the iteration method to optimize our energy function. We extensively evaluate the effectiveness of our model through experiments conducted on both synthetic and real-world datasets. Our code is available at: https://github.com/GuanyuCui/MGNN.

In this work, we study the problem of semantic communication and inference, in which a student agent (i.e. mobile device) queries a teacher agent (i.e. cloud sever) to generate higher-order data semantics living in a simplicial complex. Specifically, the teacher first maps its data into a k-order simplicial complex and learns its high-order correlations. For effective communication and inference, the teacher seeks minimally sufficient and invariant semantic structures prior to conveying information. These minimal simplicial structures are found via judiciously removing simplices selected by the Hodge Laplacians without compromising the inference query accuracy. Subsequently, the student locally runs its own set of queries based on a masked simplicial convolutional autoencoder (SCAE) leveraging both local and remote teacher's knowledge. Numerical results corroborate the effectiveness of the proposed approach in terms of improving inference query accuracy under different channel conditions and simplicial structures. Experiments on a coauthorship dataset show that removing simplices by ranking the Laplacian values yields a 85% reduction in payload size without sacrificing accuracy. Joint semantic communication and inference by masked SCAE improves query accuracy by 25% compared to local student based query and 15% compared to remote teacher based query. Finally, incorporating channel semantics is shown to effectively improve inference accuracy, notably at low SNR values.

Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system. However, due to the irregular traffic time series produced by intelligent intersections, the traffic forecasting task becomes much more intractable and imposes three major new challenges: 1) asynchronous spatial dependency, 2) irregular temporal dependency among traffic data, and 3) variable-length sequence to be predicted, which severely impede the performance of current traffic forecasting methods. To this end, we propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) to predict the traffic states of the lanes entering intelligent intersections in a future time window. Specifically, by linking lanes via a traffic diffusion graph, we first propose an Asynchronous Graph Diffusion Network to model the asynchronous spatial dependency between the time-misaligned traffic state measurements of lanes. After that, to capture the temporal dependency within irregular traffic state sequence, a learnable personalized time encoding is devised to embed the continuous time for each lane. Then we propose a Transformable Time-aware Convolution Network that learns meta-filters to derive time-aware convolution filters with transformable filter sizes for efficient temporal convolution on the irregular sequence. Furthermore, a Semi-Autoregressive Prediction Network consisting of a state evolution unit and a semiautoregressive predictor is designed to effectively and efficiently predict variable-length traffic state sequences. Extensive experiments on two real-world datasets demonstrate the effectiveness of ASeer in six metrics.

Score-based and diffusion models have emerged as effective approaches for both conditional and unconditional generation. Still conditional generation is based on either a specific training of a conditional model or classifier guidance, which requires training a noise-dependent classifier, even when the classifier for uncorrupted data is given. We propose an approach to sample from unconditional score-based generative models enforcing arbitrary logical constraints, without any additional training. Firstly, we show how to manipulate the learned score in order to sample from an un-normalized distribution conditional on a user-defined constraint. Then, we define a flexible and numerically stable neuro-symbolic framework for encoding soft logical constraints. Combining these two ingredients we obtain a general, but approximate, conditional sampling algorithm. We further developed effective heuristics aimed at improving the approximation. Finally, we show the effectiveness of our approach for various types of constraints and data: tabular data, images and time series.

Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. However, this idealistic assumption may not always hold due to potential annotation inaccuracies, meaning the ground-truth may not be present in the candidate label set. This is known as Unreliable Partial Label Learning (UPLL) that introduces an additional complexity due to the inherent unreliability and ambiguity of partial labels, often resulting in a sub-optimal performance with existing methods. To address this challenge, we propose the Unreliability-Robust Representation Learning framework (URRL) that leverages unreliability-robust contrastive learning to help the model fortify against unreliable partial labels effectively. Concurrently, we propose a dual strategy that combines KNN-based candidate label set correction and consistency-regularization-based label disambiguation to refine label quality and enhance the ability of representation learning within the URRL framework. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art PLL methods on various datasets with diverse degrees of unreliability and ambiguity. Furthermore, we provide a theoretical analysis of our approach from the perspective of the expectation maximization (EM) algorithm. Upon acceptance, we pledge to make the code publicly accessible.

Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resource-constrained devices still proved challenging. Therefore, we present an early-exiting model based on nsNet2 that provides several levels of accuracy and resource savings by halting computations at different stages. Moreover, we adapt the original architecture by splitting the information flow to take into account the injected dynamism. We show the trade-offs between performance and computational complexity based on established metrics.

Over-squashing and over-smoothing are two critical issues, that limit the capabilities of graph neural networks (GNNs). While over-smoothing eliminates the differences between nodes making them indistinguishable, over-squashing refers to the inability of GNNs to propagate information over long distances, as exponentially many node states are squashed into fixed-size representations. Both phenomena share similar causes, as both are largely induced by the graph topology. To mitigate these problems in graph classification tasks, we propose CurvPool, a novel pooling method. CurvPool exploits the notion of curvature of a graph to adaptively identify structures responsible for both over-smoothing and over-squashing. By clustering nodes based on the Balanced Forman curvature, CurvPool constructs a graph with a more suitable structure, allowing deeper models and the combination of distant information. We compare it to other state-of-the-art pooling approaches and establish its competitiveness in terms of classification accuracy, computational complexity, and flexibility. CurvPool outperforms several comparable methods across all considered tasks. The most consistent results are achieved by pooling densely connected clusters using the sum aggregation, as this allows additional information about the size of each pool.

Global sustainability requires low-carbon urban transport systems, shaped by adequate infrastructure, deployment of low-carbon transport modes and shifts in travel behavior. To adequately implement alterations in infrastructure, it's essential to grasp the location-specific cause-and-effect mechanisms that the constructed environment has on travel. Yet, current research falls short in representing causal relationships between the 6D urban form variables and travel, generalizing across different regions, and modeling urban form effects at high spatial resolution. Here, we address all three gaps by utilizing a causal discovery and an explainable machine learning framework to detect urban form effects on intra-city travel based on high-resolution mobility data of six cities across three continents. We show that both distance to city center, demographics and density indirectly affect other urban form features. By considering the causal relationships, we find that location-specific influences align across cities, yet vary in magnitude. In addition, the spread of the city and the coverage of jobs across the city are the strongest determinants of travel-related emissions, highlighting the benefits of compact development and associated benefits. Differences in urban form effects across the cities call for a more holistic definition of 6D measures. Our work is a starting point for location-specific analysis of urban form effects on mobility behavior using causal discovery approaches, which is highly relevant for city planners and municipalities across continents.

Ultrasound imaging serves as a pivotal tool for diagnosing cervical lymph node lesions. However, the diagnoses of these images largely hinge on the expertise of medical practitioners, rendering the process susceptible to misdiagnoses. Although rapidly developing deep learning has substantially improved the diagnoses of diverse ultrasound images, there remains a conspicuous research gap concerning cervical lymph nodes. The objective of our work is to accurately diagnose cervical lymph node lesions by leveraging a deep learning model. To this end, we first collected 3392 images containing normal lymph nodes, benign lymph node lesions, malignant primary lymph node lesions, and malignant metastatic lymph node lesions. Given that ultrasound images are generated by the reflection and scattering of sound waves across varied bodily tissues, we proposed the Conv-FFT Block. It integrates convolutional operations with the fast Fourier transform to more astutely model the images. Building upon this foundation, we designed a novel architecture, named US-SFNet. This architecture not only discerns variances in ultrasound images from the spatial domain but also adeptly captures microstructural alterations across various lesions in the frequency domain. To ascertain the potential of US-SFNet, we benchmarked it against 12 popular architectures through five-fold cross-validation. The results show that US-SFNet is SOTA and can achieve 92.89% accuracy, 90.46% precision, 89.95% sensitivity and 97.49% specificity, respectively.

The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs among circuit specifications. In the past decades, intensive research efforts have mostly been paid to automate the transistor sizing with a given circuit topology. By recognizing the graph nature of circuits, this paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing based on the encoder-dependent optimization subroutines. Particularly, CktGNN encodes circuit graphs using a two-level GNN framework (of nested GNN) where circuits are represented as combinations of subgraphs in a known subgraph basis. In this way, it significantly improves design efficiency by reducing the number of subgraphs to perform message passing. Nonetheless, another critical roadblock to advancing learning-assisted circuit design automation is a lack of public benchmarks to perform canonical assessment and reproducible research. To tackle the challenge, we introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains $10$K distinct operational amplifiers with carefully-extracted circuit specifications. OCB is also equipped with communicative circuit generation and evaluation capabilities such that it can help to generalize CktGNN to design various analog circuits by producing corresponding datasets. Experiments on OCB show the extraordinary advantages of CktGNN through representation-based optimization frameworks over other recent powerful GNN baselines and human experts' manual designs. Our work paves the way toward a learning-based open-sourced design automation for analog circuits. Our source code is available at \url{https://github.com/zehao-dong/CktGNN}.

The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to compact binary coalescences. While matched filtering-based techniques are known for their computational demands, existing deep learning-based methods primarily handle time-domain data and are often constrained by data duration and SNR. In addition, most existing work ignores time-delay interferometry (TDI) and applies the long-wavelength approximation in detector response calculations, thus limiting their ability to handle laser frequency noise. In this study, we introduce DECODE, an end-to-end model focusing on EMRI signal detection by sequence modeling in the frequency domain. Centered around a dilated causal convolutional neural network, trained on synthetic data considering TDI-1.5 detector response, DECODE can efficiently process a year's worth of multichannel TDI data with an SNR of around 50. We evaluate our model on 1-year data with accumulated SNR ranging from 50 to 120 and achieve a true positive rate of 96.3% at a false positive rate of 1%, keeping an inference time of less than 0.01 seconds. With the visualization of three showcased EMRI signals for interpretability and generalization, DECODE exhibits strong potential for future space-based gravitational wave data analyses.

Deep learning models often rely only on a small set of features even when there is a rich set of predictive signals in the training data. This makes models brittle and sensitive to distribution shifts. In this work, we first examine vision transformers (ViTs) and find that they tend to extract robust and spurious features with distinct attention heads. As a result of this modularity, their performance under distribution shifts can be significantly improved at test time by pruning heads corresponding to spurious features, which we demonstrate using an "oracle selection" on validation data. Second, we propose a method to further enhance the diversity and complementarity of the learned features by encouraging orthogonality of the attention heads' input gradients. We observe improved out-of-distribution performance on diagnostic benchmarks (MNIST-CIFAR, Waterbirds) as a consequence of the enhanced diversity of features and the pruning of undesirable heads.

Although remote working is increasingly adopted during the pandemic, many are concerned by the low-efficiency in the remote working. Missing in text-based communication are non-verbal cues such as facial expressions and body language, which hinders the effective communication and negatively impacts the work outcomes. Prevalent on social media platforms, emojis, as alternative non-verbal cues, are gaining popularity in the virtual workspaces well. In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces. To this end, we collect GitHub issues for a one-year period and apply causal inference techniques to measure the causal effect of emojis on the outcome of issues, controlling for confounders such as issue content, repository, and author information. We find that emojis can significantly reduce the resolution time of issues and attract more user participation. We also compare the heterogeneous effect on different types of issues. These findings deepen our understanding of the developer communities, and they provide design implications on how to facilitate interactions and broaden developer participation.

In dynamic motion generation tasks, including contact and collisions, small changes in policy parameters can lead to extremely different returns. For example, in soccer, the ball can fly in completely different directions with a similar heading motion by slightly changing the hitting position or the force applied to the ball or when the friction of the ball varies. However, it is difficult to imagine that completely different skills are needed for heading a ball in different directions. In this study, we proposed a multitask reinforcement learning algorithm for adapting a policy to implicit changes in goals or environments in a single motion category with different reward functions or physical parameters of the environment. We evaluated the proposed method on the ball heading task using a monopod robot model. The results showed that the proposed method can adapt to implicit changes in the goal positions or the coefficients of restitution of the ball, whereas the standard domain randomization approach cannot cope with different task settings.

The increasing complexity and scale of telecommunication networks have led to a growing interest in automated anomaly detection systems. However, the classification of anomalies detected on network Key Performance Indicators (KPI) has received less attention, resulting in a lack of information about anomaly characteristics and classification processes. To address this gap, this paper proposes a modular anomaly classification framework. The framework assumes separate entities for the anomaly classifier and the detector, allowing for a distinct treatment of anomaly detection and classification tasks on time series. The objectives of this study are (1) to develop a time series simulator that generates synthetic time series resembling real-world network KPI behavior, (2) to build a detection model to identify anomalies in the time series, (3) to build classification models that accurately categorize detected anomalies into predefined classes (4) to evaluate the classification framework performance on simulated and real-world network KPI time series. This study has demonstrated the good performance of the anomaly classification models trained on simulated anomalies when applied to real-world network time series data.

One approach to explaining the hierarchical levels of understanding within a machine learning model is the symbolic method of inductive logic programming (ILP), which is data efficient and capable of learning first-order logic rules that can entail data behaviour. A differentiable extension to ILP, so-called differentiable Neural Logic (dNL) networks, are able to learn Boolean functions as their neural architecture includes symbolic reasoning. We propose an application of dNL in the field of Relational Reinforcement Learning (RRL) to address dynamic continuous environments. This represents an extension of previous work in applying dNL-based ILP in RRL settings, as our proposed model updates the architecture to enable it to solve problems in continuous RL environments. The goal of this research is to improve upon current ILP methods for use in RRL by incorporating non-linear continuous predicates, allowing RRL agents to reason and make decisions in dynamic and continuous environments.  ( 2 min )

This work presents a general geometric framework for simulating and learning the dynamics of Hamiltonian systems that are invariant under a Lie group of transformations. This means that a group of symmetries is known to act on the system respecting its dynamics and, as a consequence, Noether's Theorem, conserved quantities are observed. We propose to simulate and learn the mappings of interest through the construction of $G$-invariant Lagrangian submanifolds, which are pivotal objects in symplectic geometry. A notable property of our constructions is that the simulated/learned dynamics also preserves the same conserved quantities as the original system, resulting in a more faithful surrogate of the original dynamics than non-symmetry aware methods, and in a more accurate predictor of non-observed trajectories. Furthermore, our setting is able to simulate/learn not only Hamiltonian flows, but any Lie group-equivariant symplectic transformation. Our designs leverage pivotal techniques and concepts in symplectic geometry and geometric mechanics: reduction theory, Noether's Theorem, Lagrangian submanifolds, momentum mappings, and coisotropic reduction among others. We also present methods to learn Poisson transformations while preserving the underlying geometry and how to endow non-geometric integrators with geometric properties. Thus, this work presents a novel attempt to harness the power of symplectic and Poisson geometry towards simulating and learning problems.  ( 2 min )

Large Language Model (LLM) inference consists of two distinct phases - prefill phase which processes the input prompt and decode phase which generates output tokens autoregressively. While the prefill phase effectively saturates GPU compute at small batch sizes, the decode phase results in low compute utilization as it generates one token at a time per request. The varying prefill and decode times also lead to imbalance across micro-batches when using pipeline parallelism, resulting in further inefficiency due to bubbles. We present SARATHI to address these challenges. SARATHI employs chunked-prefills, which splits a prefill request into equal sized chunks, and decode-maximal batching, which constructs a batch using a single prefill chunk and populates the remaining slots with decodes. During inference, the prefill chunk saturates GPU compute, while the decode requests 'piggyback' and cost up to an order of magnitude less compared to a decode-only batch. Chunked-prefills allows constructing multiple decode-maximal batches from a single prefill request, maximizing coverage of decodes that can piggyback. Furthermore, the uniform compute design of these batches ameliorates the imbalance between micro-batches, significantly reducing pipeline bubbles. Our techniques yield significant improvements in inference performance across models and hardware. For the LLaMA-13B model on A6000 GPU, SARATHI improves decode throughput by up to 10x, and accelerates end-to-end throughput by up to 1.33x. For LLaMa-33B on A100 GPU, we achieve 1.25x higher end-to-end-throughput and up to 4.25x higher decode throughput. When used with pipeline parallelism on GPT-3, SARATHI reduces bubbles by 6.29x, resulting in an end-to-end throughput improvement of 1.91x.  ( 3 min )

Normalizing flow is a class of deep generative models for efficient sampling and density estimation. In practice, the flow often appears as a chain of invertible neural network blocks; to facilitate training, existing works have regularized flow trajectories and designed special network architectures. The current paper develops a neural ODE flow network inspired by the Jordan-Kinderleherer-Otto (JKO) scheme, which allows efficient block-wise training of the residual blocks without sampling SDE trajectories or inner loops of score matching or variational learning. As the JKO scheme unfolds the dynamic of gradient flow, the proposed model naturally stacks residual network blocks one by one, reducing the memory load and difficulty in performing end-to-end deep flow network training. We also develop adaptive time reparameterization of the flow network with a progressive refinement of the trajectory in probability space, which improves the model training efficiency and accuracy in practice. Using numerical experiments with synthetic and real data, we show that the proposed JKO-iFlow model achieves similar or better performance in generating new samples compared with the existing flow and diffusion models at a significantly reduced computational and memory cost.

Generative models for images have gained significant attention in computer vision and natural language processing due to their ability to generate realistic samples from complex data distributions. To leverage the advances of image-based generative models for the time series domain, we propose a two-dimensional image representation for time series, the Extended Intertemporal Return Plot (XIRP). Our approach captures the intertemporal time series dynamics in a scale-invariant and invertible way, reducing training time and improving sample quality. We benchmark synthetic XIRPs obtained by an off-the-shelf Wasserstein GAN with gradient penalty (WGAN-GP) to other image representations and models regarding similarity and predictive ability metrics. Our novel, validated image representation for time series consistently and significantly outperforms a state-of-the-art RNN-based generative model regarding predictive ability. Further, we introduce an improved stochastic inversion to substantially improve simulation quality regardless of the representation and provide the prospect of transfer potentials in other domains.

In this paper, we propose a new way of remembering by introducing a memory influence mechanism for the least squares support vector machine (LSSVM). Without changing the equation constraints of the original LSSVM, this mechanism, allows an accurate partitioning of the training set without overfitting. The maximum memory impact model (MIMM) and the weighted impact memory model (WIMM) are then proposed. It is demonstrated that these models can be degraded to the LSSVM. Furthermore, we propose some different memory impact functions for the MIMM and WIMM. The experimental results show that that our MIMM and WIMM have better generalization performance compared to the LSSVM and significant advantage in time cost compared to other memory models.

This research focuses on the estimation of a non-parametric regression function designed for data with simultaneous time and space dependencies. In such a context, we study the Trend Filtering, a nonparametric estimator introduced by \cite{mammen1997locally} and \cite{rudin1992nonlinear}. For univariate settings, the signals we consider are assumed to have a kth weak derivative with bounded total variation, allowing for a general degree of smoothness. In the multivariate scenario, we study a $K$-Nearest Neighbor fused lasso estimator as in \cite{padilla2018adaptive}, employing an ADMM algorithm, suitable for signals with bounded variation that adhere to a piecewise Lipschitz continuity criterion. By aligning with lower bounds, the minimax optimality of our estimators is validated. A unique phase transition phenomenon, previously uncharted in Trend Filtering studies, emerges through our analysis. Both Simulation studies and real data applications underscore the superior performance of our method when compared with established techniques in the existing literature.

One major challenge in machine learning applications is coping with mismatches between the datasets used in the development and those obtained in real-world applications. These mismatches may lead to inaccurate predictions and errors, resulting in poor product quality and unreliable systems. In this study, we propose StyleDiff to inform developers of the differences between the two datasets for the steady development of machine learning systems. Using disentangled image spaces obtained from recently proposed generative models, StyleDiff compares the two datasets by focusing on attributes in the images and provides an easy-to-understand analysis of the differences between the datasets. The proposed StyleDiff performs in $O (d N\log N)$, where $N$ is the size of the datasets and $d$ is the number of attributes, enabling the application to large datasets. We demonstrate that StyleDiff accurately detects differences between datasets and presents them in an understandable format using, for example, driving scenes datasets.

In this paper, we first reviewed several biclustering methods that are used to identify the most significant clusters in gene expression data. Here we mainly focused on the SSVD(sparse SVD) method and tried a new sparse penalty named "Prenet penalty" which has been used only in factor analysis to gain sparsity. Then in the simulation study, we tried different types of generated datasets (with different sparsity and dimension) and tried 1-layer approximation then for k-layers which shows the mixed Prenet penalty is very effective for non-overlapped data. Finally, we used some real gene expression data to show the behavior of our methods.

Despite the widespread utilization of Gaussian process models for versatile nonparametric modeling, they exhibit limitations in effectively capturing abrupt changes in function smoothness and accommodating relationships with heteroscedastic errors. Addressing these shortcomings, the heteroscedastic Gaussian process (HeGP) regression seeks to introduce flexibility by acknowledging the variability of residual variances across covariates in the regression model. In this work, we extend the HeGP concept, expanding its scope beyond regression tasks to encompass classification and state-space models. To achieve this, we propose a novel framework where the Gaussian process is coupled with a covariate-induced precision matrix process, adopting a mixture formulation. This approach enables the modeling of heteroscedastic covariance functions across covariates. To mitigate the computational challenges posed by sampling, we employ variational inference to approximate the posterior and facilitate posterior predictive modeling. Additionally, our training process leverages an EM algorithm featuring closed-form M-step updates to efficiently evaluate the heteroscedastic covariance function. A notable feature of our model is its consistent performance on multivariate responses, accommodating various types (continuous or categorical) seamlessly. Through a combination of simulations and real-world applications in climatology, we illustrate the model's prowess and advantages. By overcoming the limitations of traditional Gaussian process models, our proposed framework offers a robust and versatile tool for a wide array of applications.

Detection and characterization of extended structures is a crucial goal in high contrast imaging. However, these structures face challenges in data reduction, leading to over-subtraction from speckles and self-subtraction with most existing methods. Iterative post-processing methods offer promising results, but their integration into existing pipelines is hindered by selective algorithms, high computational cost, and algorithmic regularization. To address this for reference differential imaging (RDI), here we propose the data imputation concept to Karhunen-Lo\`eve transform (DIKL) by modifying two steps in the standard Karhunen-Lo\`eve image projection (KLIP) method. Specifically, we partition an image to two matrices: an anchor matrix which focuses only on the speckles to obtain the DIKL coefficients, and a boat matrix which focuses on the regions of astrophysical interest for speckle removal using DIKL components. As an analytical approach, DIKL achieves high-quality results with significantly reduced computational cost (~3 orders of magnitude less than iterative methods). Being a derivative method of KLIP, DIKL is seamlessly integrable into high contrast imaging pipelines for RDI observations.  ( 3 min )

We present a framework for approximate Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained due to computational constraints, which is becoming increasingly common for applications of complex models. We model the log-likelihood function using a Gaussian process (GP) and the main methodological innovation is to apply this model to emulate the progression that an exact Metropolis-Hastings (MH) sampler would take if it was applicable. Informative log-likelihood evaluation locations are selected using a sequential experimental design strategy until the MH accept/reject decision is done accurately enough according to the GP model. The resulting approximate sampler is conceptually simple and sample-efficient. It is also more robust to violations of GP modelling assumptions compared with earlier, related "Bayesian optimisation-like" methods tailored for Bayesian inference. We discuss some theoretical aspects and various interpretations of the resulting approximate MH sampler, and demonstrate its benefits in the context of Bayesian and generalised Bayesian likelihood-free inference for simulator-based statistical models.  ( 2 min )

We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics. Such programs are common in High Energy Physics due to the presence of branching processes and clustering-based analysis. Thus differentiating such programs can open the way for gradient based optimization in the context of detector design optimization, simulator tuning, or data analysis and reconstruction optimization. We discuss several possible gradient estimation strategies, including the recent Stochastic AD method, and compare them in simplified detector design experiments. In doing so we develop, to the best of our knowledge, the first fully differentiable branching program.  ( 2 min )

This paper studies a cooperative multi-agent multi-armed stochastic bandit problem where agents operate asynchronously -- agent pull times and rates are unknown, irregular, and heterogeneous -- and face the same instance of a K-armed bandit problem. Agents can share reward information to speed up the learning process at additional communication costs. We propose ODC, an on-demand communication protocol that tailors the communication of each pair of agents based on their empirical pull times. ODC is efficient when the pull times of agents are highly heterogeneous, and its communication complexity depends on the empirical pull times of agents. ODC is a generic protocol that can be integrated into most cooperative bandit algorithms without degrading their performance. We then incorporate ODC into the natural extensions of UCB and AAE algorithms and propose two communication-efficient cooperative algorithms. Our analysis shows that both algorithms are near-optimal in regret.  ( 2 min )

Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible. While global minimization of the PDE residual over the network parameters works well for boundary value problems, catastrophic forgetting impairs the applicability of this approach to initial value problems (IVPs). In an alternative local-in-time approach, the optimization problem can be converted into an ordinary differential equation (ODE) on the network parameters and the solution propagated forward in time; however, we demonstrate that current methods based on this approach suffer from two key issues. First, following the ODE produces an uncontrolled growth in the conditioning of the problem, ultimately leading to unacceptably large numerical errors. Second, as the ODE methods scale cubically with the number of model parameters, they are restricted to small neural networks, significantly limiting their ability to represent intricate PDE initial conditions and solutions. Building on these insights, we develop Neural IVP, an ODE based IVP solver which prevents the network from getting ill-conditioned and runs in time linear in the number of parameters, enabling us to evolve the dynamics of challenging PDEs with neural networks.  ( 3 min )

Artificial Intelligence (AI) is often an integral part of modern decision support systems (DSSs). The best-performing predictive models used in AI-based DSSs lack transparency. Explainable Artificial Intelligence (XAI) aims to create AI systems that can explain their rationale to human users. Local explanations in XAI can provide information about the causes of individual predictions in terms of feature importance. However, a critical drawback of existing local explanation methods is their inability to quantify the uncertainty associated with a feature's importance. This paper introduces an extension of a feature importance explanation method, Calibrated Explanations (CE), previously only supporting classification, with support for standard regression and probabilistic regression, i.e., the probability that the target is above an arbitrary threshold. The extension for regression keeps all the benefits of CE, such as calibration of the prediction from the underlying model with confidence intervals, uncertainty quantification of feature importance, and allows both factual and counterfactual explanations. CE for standard regression provides fast, reliable, stable, and robust explanations. CE for probabilistic regression provides an entirely new way of creating probabilistic explanations from any ordinary regression model and with a dynamic selection of thresholds. The performance of CE for probabilistic regression regarding stability and speed is comparable to LIME. The method is model agnostic with easily understood conditional rules. An implementation in Python is freely available on GitHub and for installation using pip making the results in this paper easily replicable.  ( 2 min )

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (i.e., cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variation. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multi-matrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variation. We apply maRRR to gene expression data from multiple cancer types (i.e., pan-cancer) from TCGA, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variation that is shared or specific to certain cancer types.  ( 2 min )

Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential patient outcomes. Despite active research on the subject, several gaps remain: 1) proposed forecasting models have become outdated due to quick influx of advanced machine learning models (ML), 2) amount of multivariable input data has been limited and 3) discrete performance metrics have been rarely reported. In this study, we document the performance of a set of advanced ML models in forecasting ED occupancy 24 hours ahead. We use electronic health record data from a large, combined ED with an extensive set of explanatory variables, including the availability of beds in catchment area hospitals, traffic data from local observation stations, weather variables, etc. We show that N-BEATS and LightGBM outpeform benchmarks with 11 % and 9 % respective improvements and that DeepAR predicts next day crowding with an AUC of 0.76 (95 % CI 0.69-0.84). To the best of our knowledge, this is the first study to document the superiority of LightGBM and N-BEATS over statistical benchmarks in the context of ED forecasting.  ( 2 min )

Hypergraphs are important for processing data with higher-order relationships involving more than two entities. In scenarios where explicit hypergraphs are not readily available, it is desirable to infer a meaningful hypergraph structure from the node features to capture the intrinsic relations within the data. However, existing methods either adopt simple pre-defined rules that fail to precisely capture the distribution of the potential hypergraph structure, or learn a mapping between hypergraph structures and node features but require a large amount of labelled data, i.e., pre-existing hypergraph structures, for training. Both restrict their applications in practical scenarios. To fill this gap, we propose a novel smoothness prior that enables us to design a method to infer the probability for each potential hyperedge without labelled data as supervision. The proposed prior indicates features of nodes in a hyperedge are highly correlated by the features of the hyperedge containing them. We use this prior to derive the relation between the hypergraph structure and the node features via probabilistic modelling. This allows us to develop an unsupervised inference method to estimate the probability for each potential hyperedge via solving an optimisation problem that has an analytical solution. Experiments on both synthetic and real-world data demonstrate that our method can learn meaningful hypergraph structures from data more efficiently than existing hypergraph structure inference methods.  ( 3 min )

Community detection is an important content in complex network analysis. The existing community detection methods in attributed networks mostly focus on only using network structure, while the methods of integrating node attributes is mainly for the traditional community structures, and cannot detect multipartite structures and mixture structures in network. In addition, the model-based community detection methods currently proposed for attributed networks do not fully consider unique topology information of nodes, such as betweenness centrality and clustering coefficient. Therefore, a stochastic block model that integrates betweenness centrality and clustering coefficient of nodes for community detection in attributed networks, named BCSBM, is proposed in this paper. Different from other generative models for attributed networks, the generation process of links and attributes in BCSBM model follows the Poisson distribution, and the probability between community is considered based on the stochastic block model. Moreover, the betweenness centrality and clustering coefficient of nodes are introduced into the process of links and attributes generation. Finally, the expectation maximization algorithm is employed to estimate the parameters of the BCSBM model, and the node-community memberships is obtained through the hard division process, so the community detection is completed. By experimenting on six real-work networks containing different network structures, and comparing with the community detection results of five algorithms, the experimental results show that the BCSBM model not only inherits the advantages of the stochastic block model and can detect various network structures, but also has good data fitting ability due to introducing the betweenness centrality and clustering coefficient of nodes. Overall, the performance of this model is superior to other five compared algorithms.  ( 3 min )

Performance of classifiers is often measured in terms of average accuracy on test data. Despite being a standard measure, average accuracy fails in characterizing the fit of the model to the underlying conditional law of labels given the features vector ($Y|X$), e.g. due to model misspecification, over fitting, and high-dimensionality. In this paper, we consider the fundamental problem of assessing the goodness-of-fit for a general binary classifier. Our framework does not make any parametric assumption on the conditional law $Y|X$, and treats that as a black box oracle model which can be accessed only through queries. We formulate the goodness-of-fit assessment problem as a tolerance hypothesis testing of the form \[ H_0: \mathbb{E}\Big[D_f\Big({\sf Bern}(\eta(X))\|{\sf Bern}(\hat{\eta}(X))\Big)\Big]\leq \tau\,, \] where $D_f$ represents an $f$-divergence function, and $\eta(x)$, $\hat{\eta}(x)$ respectively denote the true and an estimate likelihood for a feature vector $x$ admitting a positive label. We propose a novel test, called \grasp for testing $H_0$, which works in finite sample settings, no matter the features (distribution-free). We also propose model-X \grasp designed for model-X settings where the joint distribution of the features vector is known. Model-X \grasp uses this distributional information to achieve better power. We evaluate the performance of our tests through extensive numerical experiments.  ( 2 min )

A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. When designed well, these systems promise more objective decisions while saving large amounts of resources and freeing up human time. However, when ADM systems are not designed well, they can lead to unfair decisions which discriminate against societal groups. The downstream effects of ADMs critically depend on the decisions made during the systems' design and implementation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these design decisions are made implicitly, without knowing exactly how they will influence the final system. It is therefore important to make explicit the decisions made during the design of ADM systems and understand how these decisions affect the fairness of the resulting system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit design decisions into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible "universes" of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand variability and robustness of algorithmic fairness using an exemplary case study of predicting public health coverage of vulnerable populations for potential interventions. Our results illustrate how decisions during the design of a machine learning system can have surprising effects on its fairness and how to detect these effects using multiverse analysis.  ( 3 min )

Maximum mean discrepancy (MMD) flows suffer from high computational costs in large scale computations. In this paper, we show that MMD flows with Riesz kernels $K(x,y) = - \Vert x-y\Vert^r$, $r \in (0,2)$ have exceptional properties which allow their efficient computation. We prove that the MMD of Riesz kernels coincides with the MMD of their sliced version. As a consequence, the computation of gradients of MMDs can be performed in the one-dimensional setting. Here, for $r=1$, a simple sorting algorithm can be applied to reduce the complexity from $O(MN+N^2)$ to $O((M+N)\log(M+N))$ for two measures with $M$ and $N$ support points. As another interesting follow-up result, the MMD of compactly supported measures can be estimated from above and below by the Wasserstein-1 distance. For the implementations we approximate the gradient of the sliced MMD by using only a finite number $P$ of slices. We show that the resulting error has complexity $O(\sqrt{d/P})$, where $d$ is the data dimension. These results enable us to train generative models by approximating MMD gradient flows by neural networks even for image applications. We demonstrate the efficiency of our model by image generation on MNIST, FashionMNIST and CIFAR10.  ( 2 min )

Uncovering novel drug candidates for treating complex diseases remain one of the most challenging tasks in early discovery research. To tackle this challenge, biopharma research established a standardized high content imaging protocol that tags different cellular compartments per image channel. In order to judge the experimental outcome, the scientist requires knowledge about the channel importance with respect to a certain phenotype for decoding the underlying biology. In contrast to traditional image analysis approaches, such experiments are nowadays preferably analyzed by deep learning based approaches which, however, lack crucial information about the channel importance. To overcome this limitation, we present a novel approach which utilizes multi-spectral information of high content images to interpret a certain aspect of cellular biology. To this end, we base our method on image blending concepts with alpha compositing for an arbitrary number of channels. More specifically, we introduce DCMIX, a lightweight, scaleable and end-to-end trainable mixing layer which enables interpretable predictions in high content imaging while retaining the benefits of deep learning based methods. We employ an extensive set of experiments on both MNIST and RXRX1 datasets, demonstrating that DCMIX learns the biologically relevant channel importance without scarifying prediction performance.  ( 2 min )

These lecture notes provide an overview of existing methodologies and recent developments for estimation and inference with high dimensional time series regression models. First, we present main limit theory results for high dimensional dependent data which is relevant to covariance matrix structures as well as to dependent time series sequences. Second, we present main aspects of the asymptotic theory related to time series regression models with many covariates. Third, we discuss various applications of statistical learning methodologies for time series analysis purposes.  ( 2 min )

In this article, the optimal sample complexity of learning the underlying interaction/dependencies of a Linear Dynamical System (LDS) over a Directed Acyclic Graph (DAG) is studied. The sample complexity of learning a DAG's structure is well-studied for static systems, where the samples of nodal states are independent and identically distributed (i.i.d.). However, such a study is less explored for DAGs with dynamical systems, where the nodal states are temporally correlated. We call such a DAG underlying an LDS as \emph{dynamical} DAG (DDAG). In particular, we consider a DDAG where the nodal dynamics are driven by unobserved exogenous noise sources that are wide-sense stationary (WSS) in time but are mutually uncorrelated, and have the same {power spectral density (PSD)}. Inspired by the static settings, a metric and an algorithm based on the PSD matrix of the observed time series are proposed to reconstruct the DDAG. The equal noise PSD assumption can be relaxed such that identifiability conditions for DDAG reconstruction are not violated. For the LDS with WSS (sub) Gaussian exogenous noise sources, it is shown that the optimal sample complexity (or length of state trajectory) needed to learn the DDAG is $n=\Theta(q\log(p/q))$, where $p$ is the number of nodes and $q$ is the maximum number of parents per node. To prove the sample complexity upper bound, a concentration bound for the PSD estimation is derived, under two different sampling strategies. A matching min-max lower bound using generalized Fano's inequality also is provided, thus showing the order optimality of the proposed algorithm.  ( 3 min )

Implicit neural networks have demonstrated remarkable success in various tasks. However, there is a lack of theoretical analysis of the connections and differences between implicit and explicit networks. In this paper, we study high-dimensional implicit neural networks and provide the high dimensional equivalents for the corresponding conjugate kernels and neural tangent kernels. Built upon this, we establish the equivalence between implicit and explicit networks in high dimensions.  ( 2 min )

Hi fellow computer scientists, ​ 1) I have been wondering if there is a preferred way to upsample features. I though about 3 options: ​ 1.a) Upsample Layer + Conv Layer ​ 1.b) Transposed Conv Layer + Conv Layer ​ 1.c) PixelShuffle Layer + Conv Layer ​ 2) Also, considering option 1.c, should the Conv layer multiply the number of PixelShuffle output features by the scale factor because PixelShuffle does reduce the number of output features? i.e. I have a tensor of dims (B, C, W, H, D) and with shape (1, 60, 64, 64, 64). After the Pixel-shuffle with an upscale factor of 4 I get the tensor of shape (1, 15, 256, 256, 256). Afterwards the following Conv layer should output a tensor like: 2.a) (1, 15, 256, 256, 256), where in_channels=15 and out_channels=15 2.b) (1, 60, 256, 256, 256), where in_channels=15 and out_channels=60 Note the second option reinstates the number of input features. ​ 3) I have an additional question that can happens in both 1.a, 1.b and 1.c options. Imagine I need to upsample my features by a factor of 8. 3.a) Is it preferred to have multiple upsample blocks (Upsample Layer + Conv layer), where the upsample layers have a scale factor of 2, thus for this example we would have 3 upsample blocks (2 ** 3 = 8). 3.b) Have only one upsample block where the Upsample layer has the full scale factor desired and it is then followed by one Conv layer. ​ Thank you all :) submitted by /u/Christs_Elite [link] [comments]  ( 9 min )

Hello everyone, In the process of writing up a literature review for my master's thesis, I wanted to cover the impact of ReLU on the field which was significant. When looking for an original paper I came across this paper/report: https://arxiv.org/abs/1803.08375. There isn't anything special about this work and as a matter of fact, I was surprised that it has thousands of citations (2974 at the moment of writing this post according to Google Scholar). Given this and that this work is not an original ReLU paper but more of a file documenting an implementation of it for a particular setup I found it quite intriguing. Then I started to dig into works that cited this and unexpectedly papers from top conferences such as NeurIPS cited the aforementioned document as a reference to the activation function. Here are some examples: https://proceedings.neurips.cc/paper_files/paper/2022/file/fbb10d319d44f8c3b4720873e4177c65-Paper-Conference.pdf https://proceedings.neurips.cc/paper_files/paper/2022/file/69e2f49ab0837b71b0e0cb7c555990f8-Paper-Conference.pdf The researchers who have done that are not referencing the original ReLU paper instead which I think is a bit disrespectful towards the achievement of original authors. On the other hand, maybe I am overthinking it a bit. ReLU has been around for a while and it would be surprising for someone conducting research in deep learning to not knowing it hence as a reader I wouldn't necessarily mind if people did not include the reference to the paper which is widely known. However, I reckon if a reference is made, then it should be meaningful and correct, and not just another extra few lines in a bibliography making it look big. submitted by /u/dj_giga_chinol [link] [comments]  ( 9 min )

Hi guys, I'm developing a local tool able to reply question related to one or more document. I found a good solution in using sentence embedding followed by similarity search to include only the most significative part of the document in the prompt. In this contest I search for the lightest LLM able to reply to this question. For example, LLM based on Bert are generally smaller but are they good enough? I'm not an expert in this field, I hope I give you meaningful information. Thanks! 🙏 submitted by /u/Tough-Assistant-9740 [link] [comments]  ( 9 min )

I am new to research in ML, at present a grad student and began working in a lab on my own work. My advisor is very understanding, supportive and took a leap of faith to fund me, since I did not have prior experience in research. I have been working on a problem for 4 months now and have been getting poor results for the past week. All the literature surveys, digressions within the problem statements and running the experiments to end up with not-so-good results is extremely disheartening. ​ I am still continuing to run additional experiments, figuring out where things can be going wrong and trying to conduct further analysis, but I feel like I have let down my advisor. I still have the entire semester to work on it and possibly other stuff, I am motivated for it, but at times ponder over the huge chunk of time I have spent on the current work. ​ How do you deal with such results and hitting the wall in your research ? Does it happen often ? What would you advice I do to continue working ? submitted by /u/V1bicycle [link] [comments]  ( 9 min )

https://youtube.com/shorts/mRZMOFqD0F0?si=jyHQVwq2ouAKP1t9 submitted by /u/AdGeneral5378 [link] [comments]  ( 9 min )

Hi, I'm looking to run an on-prem ChatGPT style LLM solution that can ingest private customer data into a VectorDB. So far I have tried three... GPT4All - limited to only allows for up to 13b parameter LLMs and only on CPUs (currently), also its 'localdocs' implementation I've found to only reference its docs very infrequently when answering. H2OGPT - it's implementation of localdocs (I believe via LangChain) seems pretty good. but seems like every time I run an instance, I would have to re-vector my documents. Not sure if there is a way to attach an VectorDB to it so it's ready to go right away. PrivateGPT - seems to work very well, currently it's only running on CPUs thus response time is over a minute. Curious if the community knows of any other products that do this and are already GPU accelerated. ​ TY in advance. ​ ​ submitted by /u/konrad21 [link] [comments]  ( 9 min )

Hi guys, Seeking some advice from some experienced researchers in support vector machines and kernel methods. I made this paper that breaks down using multi-class SVM in a One Against All approach, how to solve them with Lagrange multipliers https://github.com/jacobmcasey/MultiClass-SVM-Lagrange-Hyperplane-Construction-Paper As it currently stands it’s more a nice educational resource on the topic, rather than a novel contribution. Any ideas how to extend this work into something a bit more impactful? Thanks submitted by /u/Ok_Reality2341 [link] [comments]  ( 9 min )

I have key-value pairs with an extensive sequence length, alongside a sparse attention mask that is data-dependent, with fewer than 5% of its elements being non-zero. I found out that Xformer has implememation for sparse self-attention (link) but not sure whether the same would work for cross-attention. Also Xformer supports only (fixed) 2D attention mask but in my case the mask is arbitary and is different for different input. Can you suggest an efficient implementation for my scenario? submitted by /u/ankanbhunia [link] [comments]  ( 9 min )

I was going through the code for Llama-2 text generation on the official github where I stumbled across this code in the generation.py file: B_INST, E_INST = "[INST]", "[/INST]" SPECIAL_TAGS = [B_INST, E_INST, ">", ">"] UNSAFE_ERROR = "Error: special tags are not allowed as part of the prompt." ... ... ... unsafe_requests = [] unsafe_requests.append(any([tag in msg["content"] for tag in SPECIAL_TAGS for msg in dialog])) ... ... ... return [ { "generation": { "role": "assistant", "content": self.tokenizer.decode(t) if not unsafe else UNSAFE_ERROR, } } for t, unsafe in zip(generation_tokens, unsafe_requests) ] Is there a reason why we can't have these tokens in the prompt? I am planning to bypass the role based dictionary entries for the prompt and instead building my own prompt generator that'll take the the system prompts and the user prompts and generate a single string to then send to the LLM. Depending on the the user's choice I want the LLM to generate concise or detailed answers(also impose a word limit in the prompt itself), so I am planning to have this as a dropdown a user can choose. based on the system option chosen(concise/detailed answer), I then want to call my prompt generator which will add the instruction tags around the "system" and "user" prompts to generate 1 string I can then pass to the LLM. I wanted to know if there was any reason these tags aren't allowed to be in the prompt. Is it only to avoid "confusion" on the different roles and following a conventional way to pass the prompts? If not, and there's a reason those tags aren't supposed to be passed inside the prompts, please do let me know,, because inside the same file the chat_completion() function is doing exactly that; adding the > and > around the system prompts and prepending it to the user prompt. submitted by /u/comical_cow [link] [comments]  ( 10 min )

Hey everyone, ​ I am excited to present my latest venture, an initiative aimed at exploring the still-murky waters of Machine Unlearning. While this new project shares its roots with our previous endeavors in biomimetic machine learning, it diverges to concentrate on the fascinating and complex issue of algorithmic forgetfulness. ​ 🎯 **Objective** ​ The cornerstone of this project is not just to create algorithms that can forget, but to do so in a way that's both efficient and secure. Our vision transcends mere algorithmic performance, embracing a multi-faceted approach that also covers privacy protections and robust defenses against model inference attacks. The ambition here is to fortify machine unlearning with a well-rounded, secure architecture, allowing it to handle real-world …  ( 10 min )

Hello everyone! I've been working on this project for a few months as part of my thesis in Machine Learning. It's a library that provides an easy-to-use yet flexible API to design and train Diffusion Models. I decided to make it because I wanted to quickly prototype a Diffusion Model but there were no good tools to do it with. I think it really can help people prototype their own Diffusion Models a lot faster and only in a few lines of code. The idea is to have a model class that takes different modules corresponding to the different aspects of the Diffusion Model process (noise schedule, noise type, denoising network, loss function, guidance, etc.) and allow the user to mix and match different modules to achieve different results. The library ships with a bunch of prebuilt modules and the plan is to add many more. I also made it super easy to implement your own modules, you just need to extend from one of the base classes available. Below is an example of the type of interface you can expect. I'd really appreciate your feedback! Check out the project here: https://github.com/cabralpinto/modular-diffusion https://preview.redd.it/0itvswxkknlb1.png?width=2528&format=png&auto=webp&s=24ce67955eadb5cf109d19716f4e5a9471b1572d submitted by /u/secularchapel [link] [comments]  ( 9 min )

D3PM paper https://arxiv.org/pdf/2107.03006.pdf reports perplexity on LM1B dataset. In Appendix B.2 thay authors say: Perplexities are reported relative to the actual number of English-language words in the test set (including an EOS token predicted by the model) How did they compute this number? Did they split sentences by space? Are punctuation symbols considered "English words"? Are chinese characters (which are present in the data) withous spaces counted as one word? Or is it some common knowledge that "LM1B test set contains X words"? The official implementation https://github.com/google-research/google-research/tree/master/d3pm/text is extremely difficult to comprehend. I spent several hours reading throug the code and I still have no idea how they computed the number of words. submitted by /u/Tomarchelone [link] [comments]  ( 9 min )

We've just released a new version of our evolutionary hyperparameter optimization RL framework, which is 10x faster than SOTA! This update is focused on multi-agent RL. We've introduced MADDPG and MATD3 to the framework. These algorithms are traditionally super brittle, and RLlib even recommends not to use their own implementation of it. However, our evolutionary framework has solved this problem! You can now train multiple agents in co-operative or competitive Petting Zoo-style (parallel API) environments, with significantly faster training and up to 4x improvement in total return when benchmarked against alternatives. Please check it out! https://github.com/AgileRL/AgileRL submitted by /u/nicku_a [link] [comments]  ( 9 min )

Hey r/MachineLearning, data inspection and interactive exploration is one of the most important tasks for data teams. This is especially true when dealing with unstructured data that requires a deep domain expertise (e.g. healthcare or engineering). We have tried many different options for visualizing unstructured datasets in the past: Notebooks, dash apps, custom react apps, HTML reports... However, these options were either very time-consuming to develop/maintain or not interactive enough or both. That is why we developed Spotlight: https://github.com/Renumics/spotlight https://i.redd.it/lxjnlkcmumlb1.gif Spotlight supports most unstructured data types including images, audio, text, videos, time-series and geometric data. You can find more info and use case examples for ML and engineering workflows in the repo. Happy to hear your honest feedback! ​ ​ submitted by /u/44sps [link] [comments]  ( 9 min )

Hi everyone, so I am a little confused on how many target variable classes does the BERT and RoBERTa models have? So I understand these 2 models are pre-trained models, which means the number of target variable classes are fixed (if I am not wrong!). For example, the link below for the RoBERTa model in Hugging Face has fixed 3 target variable classes (Negative, Neutral and Positive): https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest But when I googled around and also asked ChatGPT and Bard, they tell me these models can have as many target variable classes as the user wants (or rather this depends on how many target variable classes there are in the training dataset). If these are pre-trained models already (which already have the number of target variable classes pre-determined in the model already), then how come some of the google sites and ChatGPT and Bard is telling me the user can choose however many target variable classes that they want? ​ submitted by /u/--leockl-- [link] [comments]  ( 9 min )

I am working on a professional project which involves detecting a vehicle moving in a wrong direction on the street. Data details : I have consecutive frames of the street in which vehicles are moving. So far : I have created a model that detects the objects inside the frame and give me the coordinates(bounding-box) of those objects(vehicles). And I using the optical flow to produce the optical lines on the objects which are moving inside the consecutive frames and I am also able to get the direction(if a object is moving from top to bottom on the frame it means it is in right direction and if bottom to top in the frame it means wrong direction based on change in y-offset of the object). Now the optical lines code is different which is giving me the direction of the object(I am not using any model to detect object in this code it's based on Lucas-Kadane method) and when I say direction I mean I'm using the cv2.imshow() which actually plays the consecutive frames together and draw optical lines on it and shows me the direction visually. Now the problem is I want the coordinates of the object that is moving in wrong direction (the bounding box coordinates) how can I achieve that? Any suggestions and ideas would be helpful please mind I can't use any other technology or model as this are project requirements in professional setting) submitted by /u/Sherlock_holmes0007 [link] [comments]  ( 9 min )

The dataset I have consists of around 2,300 observations and 120 variables, of which around 25 are highly correlated, so I narrowed it down to 95 variables. I'm using R's boost_tree() with xgboost as my model. How do I decide when to stop tuning for number of trees, mtry, min_n, and tree depth, without actually overfitting the data? Because as I increase the number of trees (or any other variable like the ones above), my RMSE obviously goes down, but how do I know it is overfitting the training data? Or is there no overfitting in this case, since I am using cross validation (15 fold) already? PS, the test data is 800 observations submitted by /u/heeeehuuuu [link] [comments]  ( 9 min )

How do you usually do feature extraction for time series data? I used to work on visual domain, so I'm pretty familiar with CVs but recently I was assigned some tasks on multivariate time series data and it's been quite difficult getting used to. Major problem is, while features in vision have semantic meaning not just along temporal axis but also "spatially," the multivariate time series does not. Also, is it considered a "cheating" if i pre-extract certain features that are already established by experts to have high correlation with the result, rather than letting the machine learning algorithm learn on its own those "certain features" in some way through training? Thanks! submitted by /u/-273deg [link] [comments]  ( 9 min )

Hi, I wonder which books or courses you would recommend for intermediate and advanced MLOps/ML design systems. What I mean is topics like handling hundreds of models and their updates, reusable CI/CD pipelines, batch and online architectures, integration with feature stores, jobs/queues for model scheduling, data drift, metrics monitoring, and alerts, and so on. This would be for someone familiar with the major concepts, hands-on experience with MLflow, SageMaker, Azure ML services, Databricks and similar tools. submitted by /u/rodrigo-arenas [link] [comments]  ( 9 min )

I have a dataset that consists of dialogues between a user and a chatbot (ChatGPT), and I want to use this data to implement Reinforcement Learning from Human Feedback (RLHF). I have already completed Supervised Fine-Tuning (SFT) and built the reward model. Now, I need some guidance on how to handle the data. Here is an example of the pre-collected data: >> User: Give me a tip on how to succeed in drawing. >>> ChatGPT: Practice regularly and be patient with yourself. Improvement takes time. >>>User: But drawing is hard. >>>ChatGPT: It is, and that's okay. It's normal to find it challenging, especially when you're just starting out. Just try to enjoy the process and don't be too hard on yourself. ​ ==========My input Data 1 is :>> User: Give me a tip on how to succeed in drawing. Suppose my model outputs the following for Input Data 1: ChatGPT: Practice makes perfect. My question is, for Input Data 2, should I use: Option A: User: Give me a tip on how to succeed in drawing. ChatGPT: Practice makes perfect. User: But drawing is hard. In this option, I use the actual previous term's agent output and append the pre-collected user data. Or Option B: User: Give me a tip on how to succeed in drawing. ChatGPT: Practice regularly and be patient with yourself. Improvement takes time. User: But drawing is hard. In this option, I use all the pre-collected data, which might not even be the current model's output. Which option is more appropriate for RLHF, A or B? submitted by /u/No_Oilve_6577 [link] [comments]  ( 9 min )

I am reading the famous paper " Unsupervised Domain Adaptation by Backpropagation" again, but still got confused why the authors had to design this gradient reversal layer. To my understanding, simply adding a minus-one (-1) in front of the domain classifier head is good enough. Of course, we need to minimize the original domain classifier head at some point to make it decent. For example, if it is a two-step training like GAN, we can (1) Freeze other parts but only minimize the domain classification loss to update the domain classifier head; and then (2) Freeze the domain classification head, but maximize the domain classificatoin loss to update the feature extractor. We can alternate between (1) and (2). Is the main motivation of gradient reversal layer that we can merge (1) and (2) into a single training step? submitted by /u/AaronSpalding [link] [comments]  ( 9 min )

Hello everyone, has anyone here read the paper "Dataset Condensation with Gradient Matching"? I've been reading it, but I got stuck trying to understand how they transition from the point where the loss is the distance between parameters to the point where the loss is the distance between gradients. Could someone please explain this process in detail? Apparently, they make the assumption that the initializations are the same and that the distance between parameters is close to zero for every iteration, but I'm still struggling to comprehend how they arrive at the conclusion that the distance is now between gradients. submitted by /u/Ok-Cartographer-1363 [link] [comments]  ( 9 min )

Hey everyone, I am excited to introduce a project that delves into the experimental fusion of Biomimicry principles with Machine Learning algorithms. While the concept of unlearning serves as our initial prototype, the overarching ambition extends far beyond, aiming to pioneer new methodologies inspired by natural phenomena. 🎯 Objective The core objective of this research is to investigate the feasibility and efficacy of incorporating biomimetic principles into machine learning algorithms. The goal is not merely to improve algorithmic performance but also to introduce novel methods that can tackle complex computational problems, much like how nature solves intricate issues in an energy-efficient manner. --- 📑 Methodological Outline **Conceptual Framework**: The project adopts a…  ( 10 min )

From his latest talk on AI, he has ever field of ML growing in market size / opportunities except for RL. Do people agree with this sentiment? Unrelated, it seems like RL nowadays is borrowing SL techniques and apply to offline datasets. submitted by /u/wardellinthehouse [link] [comments]  ( 9 min )

submitted by /u/shrekkertech [link] [comments]  ( 9 min )

​ https://preview.redd.it/ina42mr5wplb1.png?width=1298&format=png&auto=webp&s=f65fe5eade9dc1f3312ff280436e6e0a5ba6e380 submitted by /u/nimageran [link] [comments]  ( 9 min )

I have a confusion in difference between forward-view TD sampling and model-based RL. Assuming using approximation function. In forward-view TD (more than one step), the reward sampling is the future estimation in according to the currently policy (kind like searching the best situation). What is the different between the forward-view TD which likely to be planned by the policy (assuming greedy) and the model-based RL which planned by the model of fake environment? Does the only difference is model-based able to predict the result of action in 1-2-3 step in the future (in agent's head) from the transition model where model-free rely on the approx. function? submitted by /u/AnnonymeowCat [link] [comments]  ( 9 min )

Hi I'm looking to run an on-prem ChatGPT style LLM that can ingest private customer data via a VectorDB. So far I have tried three... GPT4All - limited to only allows for up to 13b parameter LLMs and only on CPUs (currently), also its 'localdocs' implementation I've found to only reference its docs very infrequently when answering. H2OGPT - it's implementation of 'localdocs' (I believe via LangChain) seems pretty good. but seems like every time I run an instance, I would have to re-vector my documents. Not sure if there is a way to attach an VectorDB to it so it's ready to go right away. PrivateGPT - seems to work very well, currently it's only running on CPUs thus response time is over a minute. Curious if the community knows of any other products that do this and are already GPU accelerated. ​ TY in advance. ​ ​ submitted by /u/konrad21 [link] [comments]  ( 9 min )

We've just released a new version of our evolutionary hyperparameter optimization RL framework, which is 10x faster than SOTA! This update is focused on multi-agent RL. We've introduced MADDPG and MATD3 to the framework. These algorithms are traditionally super brittle, and RLlib even recommends not to use their own implementation of it. However, our evolutionary framework has solved this problem! You can now train multiple agents in co-operative or competitive Petting Zoo-style (parallel API) environments, with significantly faster training and up to 4x improvement in total return when benchmarked against alternatives. Please check it out! https://github.com/AgileRL/AgileRL submitted by /u/nicku_a [link] [comments]  ( 9 min )

https://github.com/OpenLLMAI/OpenLLaMA2 We can fully fine-tune a 7B llama2 RLHF model with a Single A100 GPU. We are also developing a Ray-based RLHF framework for > 30B models. We provide PPO hyperparameters out of the box. Welcome to participate in the development and star OpenLLaMA2. ​ Some PPO vs. SFT examples. https://github.com/OpenLLMAI/OpenLLaMA2/blob/main/docs/ppo_examples.md SFT Human: Please introduce the game GTA5 Assistant: GTA5, or Grand Theft Auto V, is an action-adventure video game developed by Rockstar North and published by Rockstar Games. It was released in 2013 for the PlayStation 3 and Xbox 360, and later in 2014 for the PlayStation 4 and Xbox One. The game is set in the fictional city of Los Santos, based on Los Angeles, and follows the story of three crimi…  ( 10 min )

I have a dataset that consists of dialogues between a user and a chatbot (ChatGPT), and I want to use this data to implement Reinforcement Learning from Human Feedback (RLHF). I have already completed Supervised Fine-Tuning (SFT) and built the reward model. Now, I need some guidance on how to handle the data. Here is an example of the pre-collected data: >> User: Give me a tip on how to succeed in drawing. >>> ChatGPT: Practice regularly and be patient with yourself. Improvement takes time. >>>User: But drawing is hard. >>>ChatGPT: It is, and that's okay. It's normal to find it challenging, especially when you're just starting out. Just try to enjoy the process and don't be too hard on yourself. ​ ==========My input Data 1 is :>> User: Give me a tip on how to succeed in drawing. Suppose my model outputs the following for Input Data 1: ChatGPT: Practice makes perfect. My question is, for Input Data 2, should I use: Option A: User: Give me a tip on how to succeed in drawing. ChatGPT: Practice makes perfect. User: But drawing is hard. In this option, I use the actual previous term's agent output and append the pre-collected user data. Or Option B: User: Give me a tip on how to succeed in drawing. ChatGPT: Practice regularly and be patient with yourself. Improvement takes time. User: But drawing is hard. In this option, I use all the pre-collected data, which might not even be the current model's output. Which option is more appropriate for RLHF, A or B? submitted by /u/No_Oilve_6577 [link] [comments]  ( 10 min )

My friend has a picture of herself from a while ago with a fake tattoo. She had the tattoo made from an original image, but she doesn't have it anymore. Is there an AI that could take the tattoo from the picture that is on her body and make it into a 2d version that can be made into a tattoo guide? submitted by /u/StitchTheFox [link] [comments]  ( 9 min )

A new study cites the creation of an AI system that can predict how a specific compound will smell by analyzing its molecular structure. You can check it out here. If you want to stay on top of the latest trends and insights in AI, look here first. Why is this significant? The AI system, developed by researchers at startup Osmo, can utilize 55 descriptive words to assign a smell or 'aroma' to a chemical compound or 'odorant'. This breakthrough might be utilized to enhance the food and cleaning product industries where synthetic scents play an essential role. What’s next for this AI system? The AI's predictions often aligned closer with human consensus than any individual guess, indicating its robustness and potential. The next step for this research is to comprehend how different odorants mix and compete to yield a smell that the human brain identifies as unique. However, the sheer number of combinations, even with a small set of odorants, poses a daunting task. To quote Stuart Firestein, a neurobiologist at Columbia University, “Predicting what a mix smells like is the next frontier.” P.S. If you like this kind of analysis, you’ll love my free newsletter that tracks the most relevant news and research in AI and tech. (source) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

News provided by aibrews.com Researchers introduce ‘Swift’, the first autonomous vision-based drone that beat human world champions in several fair head-to-head races. This marks the first time that an autonomous mobile robot has beaten human champions in a real physical sport [Details]. Generative AI updates from Google Cloud Next event: General availability of Duet AI in Google Workspace [Details]. SynthID - a tool for watermarking and identifying AI images generated by Imagen (Google’s text-to-image diffusion model). It embeds a digital watermark directly into the pixels of an image, making it invisible to the human eye, but detectable for identification, without reducing the image quality [Details]. AlloyDB AI for building generative AI applications with PostgreSQL [Details]. …  ( 11 min )

submitted by /u/SAT0725 [link] [comments]  ( 9 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 9 min )

I bet there’s a genius research team out there that started work on this How cool/crazy would that be? submitted by /u/AILaunchpad [link] [comments]  ( 9 min )

This happened. Was NOT aware the already extensive and tiresome limitation in discussion subjects was THIS pervasive, and frankly, this fragile egoed. Really? THIS is "controversial?" submitted by /u/HotaruZoku [link] [comments]  ( 9 min )

submitted by /u/baillyjonthon [link] [comments]  ( 9 min )

Forget smartwatches, Microsoft may make a backpack with an AI assistant.[1] Call of Duty will use AI to moderate voice chats.[2] OpenAI Introduces Special Tutor Prompts To Implement ChatGPT In Classrooms.[3] Google Meet’s new AI will be able to go to meetings for you.[4] Sources: [1] https://www.windowscentral.com/software-apps/forget-smartwatches-microsoft-may-make-a-backpack-with-an-ai-assistant [2] https://www.theverge.com/2023/8/30/23852652/call-of-duty-activision-modulate-toxmod-artificial-intelligence-voice-moderation [3] https://robots.net/news/openai-introduces-special-tutor-prompts-to-implement-chatgpt-in-classrooms/ [4] https://www.theverge.com/2023/8/29/23849056/google-meet-ai-duet-attend-for-me submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

submitted by /u/kipaxbooks [link] [comments]  ( 9 min )

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We’re excited to announce the availability of response streaming through Amazon SageMaker real-time inference. Now you can continuously stream inference responses back to the client when using SageMaker real-time inference to help you build interactive experiences for generative AI applications such as chatbots, virtual assistants, and music generators. With this new feature, you can start streaming the responses immediately when they’re available instead of waiting for the entire response to be generated. This lowers the time-to-first-byte for your generative AI applications. In this post, we’ll show how to build a streaming web application using SageMaker real-time endpoints with the new response streaming feature for an interactive chat use case. We use Streamlit for the sample demo application UI.  ( 12 min )

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. However, bringing such solutions and models to the business-as-usual operations is not an easy task. In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps). Furthermore, we deep dive on the most common generative AI use case of text-to-text applications and LLM operations (LLMOps), a subset of FMOps. The following figure illustrates the topics we discuss.  ( 23 min )

MIT Plasma Science and Fusion Center will receive DoE support to improve access to fusion data and increase workforce diversity.  ( 8 min )

Thoughts on the Zero to Mastery programs? There is a machine learning bootcamp course on Udemy that is part of that program. I feel like i've heard negative reviews about them in the past, but it's only 12.99 right now and I feel like it covers a lot of content. So I guess I'm just wondering if it's really that bad, or if the course would be worth my time? Would it really take me from "Zero to Mastery"? Thanks submitted by /u/Mountain-Economy1476 [link] [comments]  ( 9 min )

Are there any good math for machine learning courses on Udemy? I specifically want a course that offers lots of exercises so I am able to practice what I learn. Thanks submitted by /u/Mountain-Economy1476 [link] [comments]  ( 9 min )

Hey everyone, I am excited to introduce a project that delves into the experimental fusion of Biomimicry principles with Machine Learning algorithms. While the concept of unlearning serves as our initial prototype, the overarching ambition extends far beyond, aiming to pioneer new methodologies inspired by natural phenomena. --- 🎯 **Objective** The core objective of this research is to investigate the feasibility and efficacy of incorporating biomimetic principles into machine learning algorithms. The goal is not merely to improve algorithmic performance but also to introduce novel methods that can tackle complex computational problems, much like how nature solves intricate issues in an energy-efficient manner. --- 📑 **Methodological Outline** **Conceptual Framework**: The proje…  ( 10 min )

Retrieval augmented generation (RAG) is one of the most popular way to add additional knowledge to your LLMs. To do RAG well, you need to do three things well - Curate high quality datasets Create abstractions (embeddings, keyword indexes, knowledge graphs) Stitch everything together for better retrieval We have realized that it is even harder than what it looks like. We want to easily enable this infra for a range of datasets, starting with company-specific data. You can give it a go here on our playground or get started with our open sourced library submitted by /u/achyutjoshi [link] [comments]  ( 9 min )

There was a paper submission deadline for Conference on Parsimony and Learning (CPAL) earlier this week. This is their first conference so I expect the number of submissions to be very small, but has anyone submitted? I am guessing they received like 100 or 200ish submissions. submitted by /u/neurogramer [link] [comments]  ( 9 min )

As the title says, I'm looking for a list of the best tools and framework to learn, useful for build machine learning solution as web application. I want to move my projects from being jupyter notebooks using tensorflow or pytorch, to ml API and applications. submitted by /u/AcquaFisc [link] [comments]  ( 9 min )

It has always been a struggle for me. I tried to take notes as I read paper, but that’s not quite sustainable because it’s difficult to track where did the notes come from for more details. Or I highlight the sections with added comments but that’s also not quite accessible when you have tones of pdf lying around somewhere or worse print outs. Recently I’ve been trying a cloud based pdf reader that stores my papers and allow searches over all highlights and comments (Pond) Thinking if I could also use it to share papers with my colleagues but I’m not sure if it will work because that will require them to use it as well. How do you solve this ? submitted by /u/dockerun [link] [comments]  ( 9 min )

I am working on a project for my research but need a dataset which contains the generation and consumption of electricity for Micro Hydro Power station, anyone could help me. I will be grateful submitted by /u/Due-Draft6855 [link] [comments]  ( 9 min )

https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/ From the text: We believe artificial intelligence has the power to save the world—and that a thriving open source ecosystem is essential to building this future. Thankfully, the open source ecosystem is starting to develop, and we are now seeing open source models that rival closed-source alternatives. Hundreds of small teams and individuals are also working to make these models more useful, accessible, and performant. These projects push the state of the art in open source AI and help provide a more robust and comprehensive understanding of the technology. They include: instruction-tuning base LLMs; removing censorship from LLM outputs; optimizing models for low-powered machines; building novel tooling for model inference; researching LLM security issues; and many others. However, the people behind these projects often don’t have the resources available to pursue their work to conclusion or maintain it in the long run. The situation is more acute in AI than traditional infrastructure, since even fine-tuning models requires significant GPU computing resources, especially as open source models get larger. ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2308.16137 Abstract: In recent years, there have been remarkable advancements in the performance of Transformer-based Large Language Models (LLMs) across various domains. As these LLMs are deployed for increasingly complex tasks, they often face the needs to conduct longer reasoning processes or understanding larger contexts. In these situations, the length generalization failure of LLMs on long sequences become more prominent. Most pre-training schemes truncate training sequences to a fixed length (such as 2048 for LLaMa). LLMs often struggle to generate fluent texts, let alone carry out downstream tasks, after longer contexts, even with relative positional encoding which is designed to cope with this problem. Common solutions such as finetuning on longer …  ( 9 min )

CoTracker - a 2D point-tracking tool for videos - promises to revolutionize motion tracking. Through the use of a transformer network, it meticulously predicts point trajectories and visibility across video frames, giving insights like never before. https://i.redd.it/g0u5t9n1ehlb1.gif Here's why CoTracker is turning heads: CoTracker leverages advanced transformer formulation: Utilising a grid of input tokens that evolve to output tokens, CoTracker allocates initial values derived from the track's start point and time. It's built to handle extended videos through 'windowed inference': Windowing enables the algorithm to handle videos beyond its maximum window length by splitting them into overlapping segments. 'Unrolled Learning' caters to semi-overlapping windows effectively: By employing two unique types of losses, only a modest amount of windows are used in loss computation while still handling expansive videos at test time. Improved tracking through simultaneous multi-point selection: By tracking multiple points at once, CoTracker is able to better establish correlation and motion paths within videos. Despite its notable strengths, there are limitations. Its sliding-window approach cannot handle long-term occlusions that last longer than a window, and its transformer-based model has a high computational cost that grows quadratically with the number of tracked points. According to the authors, “The result is a flexible and powerful tracking algorithm that outperforms state-of-the-art methods in almost all benchmarks”. But it’s yet to be seen how it will perform in real-life tasks. What do you think? P.S. If you like this type of analysis, you might want to check this out. (arXiv) (GitHub) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

All of this is framed through the lens of improving your ability to understand if a prediction for an upcoming UFC match is good or not. Happy to dig further into the ML and processing around this. https://blog.wolftickets.ai/teaching-a-wolf-to-speak-transforming-fight-predictions-into-insights.html Any feedback is appreciated! submitted by /u/wolfticketsai [link] [comments]  ( 9 min )

Hi everyone, Wanted to share an open source project we've been working on for the last few weeks: Autolabel is an open source Python library to label and enrich text datasets with LLMs (Large Language Models). Why? Access to clean, labeled data is a huge bottleneck for most ML/data science teams. From experiments across a variety of NLP tasks and datasets, we have found that the most capable LLMs are able to label data at better quality than human annotators, but 20-100x faster. Getting Started¶ You can get started with the library by defining a JSON config, and writing a few lines of code: from autolabel import LabelingAgent, AutolabelDataset agent = LabelingAgent('config.json') dataset = AutolabelDataset('dataset.csv', 'config.json') labels = agent.run(dataset) Installation guide Sample notebooks that show how to use the library for different labeling tasks. Technical report for benchmarking LLM and human annotator performance across a range of tasks and datasets. Call for Feedback We just open sourced this library, and are actively developing it. Feedback is very welcome and so are requests for features. You can open an issue on Github for bugs and request features submitted by /u/nihit-d [link] [comments]  ( 9 min )

In Equation 1 of the paper "Axiomatic Attribution for Deep Networks", the denominator of the gradient is $\partial x_i$ (See Eq1). However, according to the paper(with Eq2), shouldn't it be $\partial (x'_i + \alpha \times (x_i-x'_i))$ rather than $\partial x_i$? I found many following papers which refer to this paper also use the notation like this. Do I misunderstand something? submitted by /u/qjall [link] [comments]  ( 9 min )

In the realm of deep learning, the choice of activation functions and optimization algorithms can significantly impact the training process and the performance of neural networks. A relatively lesser-known gem in this landscape is the "leaky softplus" activation function, which, when paired with momentum-based optimizers like Adam, can lead to exceptionally efficient and effective training outcomes. The Leaky Softplus Activation Function The leaky softplus activation function combines the benefits of both linearity and non-linearity in a graceful manner. Defined as Math.Log(Math.Exp(x) + 1) + (x / 16), it smoothly transitions between a nearly linear response for negative inputs and a more pronounced non-linear response for positive inputs. This unique characteristic enables it to address…  ( 10 min )

Hi everyone, I built DeepEval - an open-source unit testing framework for LLMs in order to accelerate development and iteration. The problem When designing software applications, testing has always been critical for a lot of production applications. However - with the rise of LLM applications, the type of testing required needs to change in order to adapt for the large number of possible queries. We therefore built DeepEval in order to make it easy to write LLM tests in just 1 line of code. We hope this solution is of value to future teams when iterating on their RAG pipelines, migrating LLM models, testing their fine-tuned LLMs. The solution The DeepEval framework is as follows: We split up testing LLMs into 4 main sections: - Answer Relevancy (how relevant an answer is to a question) - measured using a question-answer bi-encoder. - Factual consistency (whether the generated answer is hallucinating) - measured using entailment from an NLI model - Conceptual similarity (when given a ground truth, how closely does it relate to it - for example How big is it? The size of an orange vs 20 square centimetres.) - measured using vector similarity - Bias, Toxic classification (measured through DL classifier models) I would love any feedback on what we are building here and welcome any OS contributions! submitted by /u/ConfectionSafe954 [link] [comments]  ( 9 min )

I've been wanting to take a code chatbot model like starchat or codellama and tune it to our codebase, problem is all I have at work is a Mac with 8gb of RAM. I talk with my boss today and can ask for some stuff if I want and can give good reason. What's the most efficient way to get the compute I need to train the model. Any other advice on how to go about doing this is greatly appreciated submitted by /u/Kechup17 [link] [comments]  ( 9 min )

Meta AI has made their DINOv2 self-supervised learning method for training computer vision models truly open source by publishing it under Apache 2.0 license. DINOv2 has outperformed previous state-of-the-art self-supervised learning methods on a variety of computer vision tasks, including image classification, object detection, and semantic segmentation. It is also more efficient to train than previous methods, making it more accessible to researchers and practitioners. DINOv2 is different from existing methods because it provides a new way to train high-performance computer vision models without the need for labeled data. This makes it possible to train models on large datasets of unlabeled images, which can be more cost-effective and time-efficient than collecting and labeling large datasets of images. New demo: https://dinov2.metademolab.com/ submitted by /u/noiseinvacuum [link] [comments]  ( 9 min )

I have experience in deep learning but am a beginner in using deep reinforcement learning for robotics. However, I have recently gone through the huggingface course on deep reinforcement learning. I tried tinkering around with panda-gym but am having trouble trying to start my own project. I am trying to use two UR5 robots do some bimanual manipulation tasks e.g. have the left arm hold onto a cup while the right pours water into it. panda-gym allows me to import a URDF file of my own robot but I can't find the option to import my own objects like the xml file (or any extension) of a table or a water bottle. I have no idea which library allows me to import multiple URDF robots and xml objects and was hoping for some help. EDIT : I actually just read about Gazebo and was wondering if it'll allow me to do the above ? As a beginner I still have zero experience with ros and gazebo. submitted by /u/I_am_a_robot_ [link] [comments]  ( 9 min )

As everyone knows, we usually optimise for maximum likelihood when fitting distributions like gaussians (equivalent to the forward KL-divergence). But for neural networks, techniques like GANs allow the minimisation of other distances like Mutual Information or Reverse KL. While this is certainly a very cool and insightful approach, it's also highly complex. I wonder wether other approaches to this problem exist for the simpler case, like fitting a gaussian or some other analytic distribution. From statistics, I have only encountered maximum likelihood and it's variations like robust statistics. submitted by /u/LeanderKu [link] [comments]  ( 9 min )

This is the package https://github.com/mfumagalli68/xi-method Follow the README and install directly from pypi. From the paper: " [..]To bridge this gap we propose a family of measures of statistical association whose definition is well-posed also for nonordered data. Our intuition is to rely on separation measurements between probability mass functions. Here, by separation measurement we mean any distance or divergence between probability mass functions that is positive, and that is null if and only if the probability mass functions coincide. Then, we show that the new class of sensitivity indices complies with Renyi’s postulate D of measures of statistical dependence (Renyi, 1959). This postulate, called zero-independence property in the following, requires that a measure of associat…  ( 10 min )

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Hello all, I am trying to solve the frozen lake environment using DQN. And I see two issues. One is that the loss falls down to zeros and second the agent only reaches the goal only 5 times in 1000 epochs. Here's my code. import numpy as np import tensorflow as tf from tensorflow.keras import layers, activations import matplotlib.pyplot as plt import gym def create_agent(num_inputs, num_outputs, layer1, layer2): inputs = layers.Input(shape=(num_inputs, )) hidden1 = layers.Dense(layer1)(inputs) activation1 = activations.relu(hidden1) hidden2 = layers.Dense(layer2)(activation1) activation2 = activations.relu(hidden2) outputs = layers.Dense(num_outputs, activation='linear')(activation2) model = tf.keras.Model(inputs, outputs) return model loss_mse = tf.keras.losses.MeanSquaredError() lear…  ( 10 min )

submitted by /u/gwern [link] [comments]  ( 9 min )

I have experience in deep learning but am a beginner in using deep reinforcement learning for robotics. However, I have recently gone through the huggingface course on deep reinforcement learning. I tried tinkering around with panda-gym but am having trouble trying to start my own project. I am trying to use two UR5 robots do some bimanual manipulation tasks e.g. have the left arm hold onto a cup while the right pours water into it. panda-gym allows me to import a URDF file of my own robot but I can't find the option to import my own objects like the xml file (or any extension) of a table or a water bottle. I have no idea which library allows me to import multiple URDF robots and xml objects and was hoping for some help. submitted by /u/I_am_a_robot_ [link] [comments]  ( 9 min )

Hi I am struggling to understand the mini-batch in PPO. Say I already collected two trajectories. Traj_A = [t = 1, t= 2, t=3 ,.... t = 100] Traj_B = [t =1 , t=2, ... t= 78] Now, I heard you usually break this down onto mini-batch (say a batchsize of 6). Do you do random sampling? eg, one batch is [Traj_A_t=1, Traj_A_t=2, Traj_A_t=100, Traj_A_t=66, Traj_A_t=77, Traj_A_t=55]??? OR do you need to maintain some sequence [Traj_A_t=1, Traj_A_t=2, Traj_A_t=3, Traj_A_t=4, Traj_A_t=5, Traj_A_t=6]??? submitted by /u/No_Oilve_6577 [link] [comments]  ( 9 min )

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The US government has imposed expanded export restrictions affecting Nvidia’s leading artificial intelligence chips, curbing their exportation beyond China to certain Middle Eastern countries. If you want to stay on top of AI advances, look here first. https://preview.redd.it/xe7ho00t0ilb1.png?width=1240&format=png&auto=webp&s=61225931bf3e316efd90eab83846402d4148aca2 Why this matters: Nvidia’s A100 and H100 chips are affected: These AI chips are important and used to accelerate machine-learning tasks on major AI applications like ChatGPT. Despite the restrictions, Nvidia maintains they won’t have an “immediate material impact” on its results. Other companies, like AMD, are also affected: They’ve reportedly received similar restrictions notice, hinting at a broader move by the US government to control the distribution of AI chip technology. The move is part of a larger geopolitical play: These restrictions form part of the Biden administration’s efforts to curtail Beijing’s ability to capitalize on the AI revolution. How Nvidia and the industry might respond: Nvidia CEO Jensen Huang has cautioned the US: In a Financial Times interview, Huang warned that imposing such restrictions could lead to “enormous damage” to the US tech industry, predicting China may become self-sufficient in AI chip development. Yet, Nvidia still managed impressive earnings recently: Despite these challenges, Nvidia recently reported quarterly revenue of $13.5bn, exceeding predictions by $2bn. Further restrictions could significantly alter the landscape for AI development, potentially fostering greater innovation in countries affected or even a race to develop independent solutions. P.S. If you like this kind of analysis, you might want to check this out. (source) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

In a bid to rival the United States’ stronghold in the AI industry, Chinese search engine and AI firm Baidu, has made its ChatGPT-equivalent language model, Ernie Bot, fully available to the public. This marks a significant move on the AI chessboard. If you want to stay on top of everything AI, look here first. https://preview.redd.it/g68sr07iihlb1.jpg?width=1024&format=pjpg&auto=webp&s=c0c873badd448257bcc2fb125188acc198e504d6 Why does this matter? Baidu's public release of Ernie Bot signals the company's aggressive push in the generative AI market. By opening up its model to the public, Baidu can leverage expansive real-world human feedback to improve Ernie Bot. China's determination to lead the AI industry is unabated, with many tech firms launching their own generative models in response to OpenAI's popular ChatGPT. Baidu's move further fuels this rivalry. Regulation in China seems to support such AI advancements. CEO Robin Li voiced his optimism about the AI regulations—calling them "more pro-innovation than regulation". What's the broader response? Baidu's latest stride has boosted its stock price by over 3%, underlining the market's high anticipation of Baidu's AI efforts. Ernie Bot has rocketed to the top of Apple's iOS free app chart in China. This demonstrates a positive initial response from the public. Regulation is key in China's AI game: China has stringent regulations for the generative AI industry, requiring a security review and government approvals before any product launch. Moreover, companies need to comply with governmental tech and data requests. The US, on the other hand, doesn't currently have such regulations in place. A markedly different approach that could significantly influence the development and application of AI technologies. If you like this kind of analysis, you might want to check this out. (source) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

I'm looking for a AI Search tool that replaces the search bar on a website. Search tool will scrape that sites data and offer suggestions. Any recommendations? submitted by /u/CauliflowerTiny1454 [link] [comments]  ( 9 min )

submitted by /u/Sonic_Improv [link] [comments]  ( 9 min )

I'm currently researching how users interact with ChatGPT and its features, and I'd really appreciate your insights, experience, and perspective. Why should you participate? It's a quick 5-minute survey. Your identity and responses are completely anonymous. Your input will significantly contribute to important research on ChatGPT. The final research document will be posted to this sub. Survey Link: https://forms.gle/tNBib2dA1ErFEwbk6 Rest assured, all information will be confidential and only used for the purpose of this research. Thank you for your time submitted by /u/aaron-cesaro [link] [comments]  ( 9 min )

So yeah it's an open book course, but I'm horrible at flow and grammar. I need to be able to fix these things without getting in trouble. Ten years ago in my undergrad friends and family would do the final proofreading for me to make small changes. Is undetectable reputable. submitted by /u/6ixsideOT [link] [comments]  ( 9 min )

​ ChatFAI characters Hey everyone, ChatFAI has a special connection with this community because this is where I got it started. It was a simple web app that allowed you to interact with your favorite characters from movies, TV shows, books, history, and beyond. Now, it is a lot more. It has public APIs and an official Discord bot integration now. A lot of performance improvements have been made in the recent days. People have created a lot of characters (https://chatfai.com/characters) The Discord bot is still a new area so could you share feedback if you guys check it out? You can also find it in the Discord app directory. submitted by /u/usamaejazch [link] [comments]  ( 9 min )

submitted by /u/Tao_Dragon [link] [comments]  ( 9 min )

Tesla is about to flip the switch on its $300 million new AI cluster, featuring 10,000 Nvidia H100 compute GPUs.[1] Intel has revealed two new Intel Xeon processors this week at Hot Chips 2023 to give designers new options for efficient server-level performance.[2] General Motors is using conversational AI chatbots to handle simple OnStar calls, freeing up the service’s human employees to address more complex requests, the company said Tuesday.[3] Microsoft announces Turing Bletchley v3 vision-language model for Bing image searches.[4] Sources: [1] https://www.tomshardware.com/news/teslas-dollar300-million-ai-cluster-is-going-live-today [2] https://www.allaboutcircuits.com/news/intel-reveals-two-new-xeon-processor-lines-at-hot-chips-2023/ [3] https://www.theverge.com/2023/8/29/23849390/gm-google-cloud-ai-chat-bot-onstar [4] https://www.neowin.net/news/microsoft-announces-turing-bletchley-v3-vision-language-model-for-bing-image-searches/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

submitted by /u/sardoa11 [link] [comments]  ( 9 min )

The US government is taking steps to address the complex and controversial issues around AI and intellectual property rights. The US Copyright Office is opening a public comment period on August 30th to hear from various stakeholders on the topic. Important Details: The agency is asking for comments on three main questions: How should AI be defined and categorized for the purposes of copyright? What are the implications of AI for the rights of authors and owners of works? What are the implications of AI for the liability and responsibility of users and distributors of works? The agency also wants to hear about related issues, such as: how AI may affect publicity rights and unfair competition laws. The agency notes that AI may create works that mimic or impersonate the voices, likenesses, or styles of real people, which could raise ethical and legal concerns. Finally, they want to determine how AI may affect moral rights and cultural heritage: The agency acknowledges that AI may create works that are derivative or transformative of existing works, which could affect the reputation and integrity of the original creators and their communities. The deadline to submit your comments is October 18th and specific instructions for submitting comments are available on the Copyright Office website. P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI and tech—stay updated in under 3 mins/day. (source) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

I was playing around with the Eleven labs V2 multilingual model and I have to say it’s extremely impressive. Does this sound like the real Tucker? submitted by /u/Exitium_Maximus [link] [comments]  ( 9 min )

In his book How to Read Literature Like a Professor, Thomas Foster says that if a poem looks like a square on the printed page, it’s likely a sonnet. The miracle of the sonnet, you see, is that it is fourteen lines long and written almost always in iambic pentameter. … suffice it to say […] Sonnets are square first appeared on John D. Cook.  ( 4 min )

One of the tools available as part of the ML governance is Amazon SageMaker Model Cards, which has the capability to create a single source of truth for model information by centralizing and standardizing documentation throughout the model lifecycle. SageMaker model cards enable you to standardize how models are documented, thereby achieving visibility into the lifecycle of a model, from designing, building, training, and evaluation. Model cards are intended to be a single source of truth for business and technical metadata about the model that can reliably be used for auditing and documentation purposes. They provide a fact sheet of the model that is important for model governance.  ( 10 min )

One of the tools available as part of the ML governance is Amazon SageMaker Model Cards, which has the capability to create a single source of truth for model information by centralizing and standardizing documentation throughout the model lifecycle. SageMaker model cards enable you to standardize how models are documented, thereby achieving visibility into the lifecycle of a model, from designing, building, training, and evaluation. Model cards are intended to be a single source of truth for business and technical metadata about the model that can reliably be used for auditing and documentation purposes. They provide a fact sheet of the model that is important for model governance.  ( 10 min )

Posted by Stephan Rasp, Research Scientist, and Carla Bromberg, Program Lead, Google Research In 1950, weather forecasting started its digital revolution when researchers used the first programmable, general-purpose computer ENIAC to solve mathematical equations describing how weather evolves. In the more than 70 years since, continuous advancements in computing power and improvements to the model formulations have led to steady gains in weather forecast skill: a 7-day forecast today is about as accurate as a 5-day forecast in 2000 and a 3-day forecast in 1980. While improving forecast accuracy at the pace of approximately one day per decade may not seem like a big deal, every day improved is important in far reaching use cases, such as for logistics planning, disaster management, agr…  ( 93 min )

Entrepreneurs are cultivating generative AI from the west coast of Africa to the eastern edge of the Arabian Desert. Gen AI is the latest of the big plans Kofi Genfi and Nii Osae have been hatching since they met 15 years ago in high school in Accra, Ghana’s capital that sits on the Gulf of Read article >  ( 7 min )

Academics Mory Gharib and Alireza Ramezani in 2020 were spitballing a transforming robot that is now getting a shot at work that’s literally out of this world: NASA Mars Rover missions. Caltech has unveiled its multi-talented robot that can fly, drive, walk and do eight permutations of motions through a combination of its skills. They Read article >  ( 6 min )

Just like that, summer falls into September, and some of the most anticipated games of the year, like the Cyberpunk 2077: Phantom Liberty expansion, PAYDAY 3 and Party Animals, are dropping into the GeForce NOW library at launch this month. They’re part of 24 new games hitting the cloud gaming service in September. And the Read article >  ( 8 min )

In this episode of the Microsoft Research Podcast, Managing Director of Microsoft Research India Sriram Rajamani discusses how generative AI is impacting the lab’s approach to research and how the country’s many languages can help advance conversational systems. The post AI Frontiers: AI in India and beyond with Sriram Rajamani appeared first on Microsoft Research.  ( 30 min )

Data is crucial in most industries today. As the amount of business information grows, so do the standards for people’s protection of their personal information. With advanced cyberattacks, security compliance frameworks and cybersecurity have become essential fields to ensure data is collected, organized, stored, and managed in a safe way. This article will start by… Read More »4 data compliance standards to know for 2023 The post 4 data compliance standards to know for 2023 appeared first on Data Science Central.  ( 24 min )

Large language models (LLMs) fit parameters (features in data topography) to a particular dataset, such as text scraped off the web and conformed to a training set.  Logical data models (LDMs), by contrast, model what becomes shared within entire systems. They bring together the data in a system with the help of various kinds of… Read More »How the LDMs in knowledge graphs can complement LLMs The post How the LDMs in knowledge graphs can complement LLMs appeared first on Data Science Central.  ( 21 min )

submitted by /u/recklessdesuka [link] [comments]  ( 9 min )

I work in science research and want to introduce new tools to my students. We are looking for AI that can read tables, charts, figures, and spreadsheets, and possibly run statistics on this information. We are also looking for AI that can be given a prompt and will write on chosen topic with proper citation of sources. This information will not be used for publication, but rather, to organize main ideas and provide examples. An art AI that can draw or mimic images of real insects would be nice as well. Preferably these will all be free to use. submitted by /u/wolfmonarchyhq [link] [comments]  ( 9 min )

submitted by /u/stefanbg92 [link] [comments]  ( 9 min )

Recent research proposes that Large Language Models (LLMs) may not be as reliable as we think. In fact, the order of options in a multiple-choice question drastically influences the responses from LLMs such as GPT-4 and InstructGPT. If you want to stay on top of the latest trends and insights in AI and tech, look here first. https://preview.redd.it/dxfsq72kzalb1.png?width=1289&format=png&auto=webp&s=e4ed5b541073bde18d2865f2c15e8028388070f5 What are the findings? LLM sensitivity to multiple-choice arrangement: The study suggests if options in multiple-choice questions are reordered, the LLM's performance varies dramatically— approximately 13% to 75% depending on the benchmark. Positional bias shapes responses: When the LLM is uncertain between top-selected answers, the option positioning can artificially lean its predictions. Observations also found that LLMs favor specific placements when unsure of the optimal response among top-selected answers. Performance improves when calibration techniques are applied: Making use of two unique calibration methods, the performance of LLMS saw up to eight percentage points of increase across numerous models and benchmarks. Why does this matter? This moves us closer to identifying the factors contributing to LLMs' sensitivity and highlights the significance of recognizing and confronting these sensitivities to improve real-world usability and reliability. P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI and tech—stay updated in under 3 mins/day. (arXiv) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

AI can also be dangerous because it could make automated trades in the stock market or cryptocurrency market, and because it remembers all the exchange rate changes in history and the entire economic history of the world and also has all the statistics and mathematical knowledge at its fingertips, it can easily draw conclusions and create an algorithm that might make you bigger profits than any real human. He could also learn from his own mistakes and keep improving. Is this possible? Are there any AIs like this already? submitted by /u/Steve_Hufnagel [link] [comments]  ( 9 min )

OpenAI is reportedly making strides in its financial performance and is on track to make $1 billion in revenue over the next 12 months, as per recent reports by The Information. This is a major milestone, signifying not only the success of OpenAI but also the increasing demand and investment in AI. If you want to stay on top of the latest trends and insights in AI and tech, look here first. https://preview.redd.it/afak3xrevalb1.jpg?width=660&format=pjpg&auto=webp&s=cd8f20ac732618b91dbf96928ede42d693f6c4a9 Why should we pay attention? Setting expectations: The Information estimates OpenAI's monthly revenue to be around $80 million, in line with the $1 billion yearly revenue prediction. Undeniably, OpenAI is accelerating. AI Chatbots are in high demand: ChatGPT, OpenAI's phenomenal co…  ( 10 min )

Wearable Health Solutions to Advise Next Realm AI on Medical Internet of Things (MIoT) Solutions NEWPORT BEACH, CA / ACCESSWIRE / Wearable Health Solutions Inc. (OTC PINK:WHSI) announced inclusion to Next Realm AI research lab to explore development of healthcare IoT solutions utilizing data analytics and artificial intelligence (AI). Wearable Healthcare Solutions will collaborate and advise Next Realm AI, an artificial intelligence and data analytics research lab located in New York City, on such areas as collecting and developing data solutions within the areas of wearables, IoT, and Medical Internet of Things (MIoT). As an official IBM Business Partner, Next Realm AI assists lab members in integrating leading-edge AI and data solutions into their business operations. By leveraging Next Realm's expertise, clients can modernize processes, boost efficiency, strengthen security, and deliver greater value to customers - all while driving growth and building value. https://www.otcmarkets.com/stock/whsi/news/Wearable-Health-Solutions-to-Advise-Next-Realm-AI-on-Medical-Internet-of-Things-MIoT-Solutions?id=411692 submitted by /u/NextRealm_AI [link] [comments]  ( 9 min )

I am 23 year old man, I have a degree in Politics, Philosophy, & Economics. Next year I want to do a masters degree, but I haven't chosen which one yet. I am both fascinated by AI, and want to be future-proof in my education. What potential careers do you see, currently or in the near future, in the field of AI, and what studies would you recommend to be well prepared for them? ​ submitted by /u/ApplePenguinBaguette [link] [comments]  ( 9 min )

IBM’s CEO, who froze hiring for thousands of back-office jobs and predicted A.I. would take up to 50% of new jobs, just piled into a $4.5 billion tech unicorn’s massive new $235 million funding round submitted by /u/AminoOxi [link] [comments]  ( 9 min )

New advances in AI hardware are making the singularity more likely. AI systems will be able to learn and process information much faster, which could lead to a breakthrough in AI capabilities. These advancements include quantum computing and neuromorphic computing, but more specifically the rise of affordable models like NVIDIA H100 and, more recently, GH200 models. If you are interested in this kind of information, there are more details here. submitted by /u/Powerful-Pumpkin-938 [link] [comments]  ( 9 min )

Where do websites like made.porn, pornify.cc or porn.ai get their AI models from? submitted by /u/mixedfeelingz [link] [comments]  ( 9 min )

Like custom world descriptions, AI apps/sites, etc submitted by /u/roblox22g [link] [comments]  ( 9 min )

submitted by /u/Agitated-Spell3979 [link] [comments]  ( 9 min )

Tesla's making a significant power move today as it prepares to bring its brand-new new AI-cluster online. Rocking a hefty 10,000 Nvidia H100 compute GPUs, the machine will tackle high-performance computing (HPC) workloads and AI applications, placing Tesla's capabilities among the global AI elite. If you want to stay on top of the latest trends and insights in AI and tech, look here first. https://preview.redd.it/c5ykgmr6r6lb1.png?width=970&format=png&auto=webp&s=8b830c8754c1a11792149f57d19a57a77fc8b161 Here’s why this matters: This Nvidia H100-based AI supercomputer will be one of the most powerful globally. With a peak performance of 340 FP64 PFLOPS and 39.58 INT8 ExaFLOPS for AI programs, even Leonardo, currently the fourth highest-performing supercomputer, is surpassed. Tesla’s…  ( 10 min )

Research firm SemiAnalysis has declared that Google’s anticipated Gemini AI model will smash OpenAI’s offering by packing a lot more computing power.[1] DoorDash today announced its development of voice ordering capabilities incorporating AI, building on its existing model leveraging best-in-class agents, to further support restaurant operations.[2] The US Air Force wants $6 billion to build a fleet of AI-controlled drones.[3] Google’s DeepMind says it has cracked a problem that has vexed those trying to verify whether images are real or created by AI. Researchers proclaimed their new watermarking SynthID format can be used to pinpoint AI-generated deepfakes without distorting the image’s original quality.[4] Sources: [1] https://beincrypto.com/ai-wars-google-gemini-chatgpt/ [2] https://about.doordash.com/en-us/news/introducing-ai-and-agent-powered-voice-ordering [3] https://www.engadget.com/the-air-force-wants-6-billion-to-build-a-fleet-of-ai-controlled-drones-204548974.html [4] https://gizmodo.com.au/2023/08/deepmind-says-it-has-a-way-to-identify-ai-images-but-only-on-google/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Microsoft teamed up with Virginia Tech to publish a white paper introducing their new "Algorithm of Thoughts" (AoT). The objective? To make language learning models akin to human learning. https://preview.redd.it/3q8lcq0k96lb1.png?width=2000&format=png&auto=webp&s=fce7a0e3225c64353ad6c51f65e8b490e795feed If you want to stay on top of the latest trends and insights in AI and tech, look here first. What's the big idea? Microsoft's AoT aims to fuse the accuracy of algorithms with the nuances of human reasoning. A bold aspiration indeed, but not a new one. The goal to empower computers to learn for themselves – akin to human cognition - has been an AI objective since its inception back in the 1950s. The AoT could be seen as an attempt to resolve the drawbacks of the "Chain of Thought" (CoT) approach. LLMs following the CoT approach can provide incorrect steps to the right answer, as they base conclusions on precedent. With AoT, the model works to evaluate the soundness of initial steps or "thoughts," reducing the risk of one incorrect step leading to disproportionate results. What could AoT do? Mitigate AI "hallucinations:" These funny— but disconcerting — instances of AI outputting false information. Enhance the integrity of AI interaction: programmers suggest that improvement in this aspect is crucial for aligning AGI (artificial general intelligence). The takeaway: AI's ability to understand and process information like a human being is a longstanding goal in the field. With AoT, Microsoft seems to be making strides toward achieving it. Much remains to be seen on its efficacy: How it will impact the broader AI ecosystem and the user experiences it can create. P.S. If you like this kind of analysis, I write a free newsletter tracking the most relevant news and research in AI and tech—stay informed in under 3 minutes/day. (source) ​ submitted by /u/AIsupercharged [link] [comments]  ( 10 min )

submitted by /u/nickb [link] [comments]  ( 9 min )

TLDR: American, Graduated from U of Michigan in 2019 w/ 3.3 GPA Bs in Comp Sci. Worked at Google for 3 years. Samsung Research America for 3 months. No ML specific work experience. No research. Will I get in to European elite ML programs? If not what do I need to do? My GF and I want to study our masters together in Europe. She’s doing business I want to do ML/AI. I spent the past year kind of goofing off. Got kind of burned out and decided I was going to get into music production so spent the pst year mainly doing that with some software mixed in. Recently been self studying ML, both the math from textbooks and trying my hand at some models in python. I do not have any connections to academia currently and wil have to beg a professor who barely knew me from undergrad for a rec. Can get other recs from past bosses. My plan right now is to look for job hopefully in AI but maybe just more general software engineering again, but long term I want to get a masters in person. My current resume looks like: Graduated BS in Comp Sci from univ of Michigan 2019 3.3 GPA Worked at Google for 3 years Worked at Samsung Research America for 4 months Some self study I can claim but not much tangible proof Recommendation from Google Boss (Maybe) recommendation from UofM CS professor that barely knew me My questions to anyone that knows the admissions right now are: 1) Do you think I get to one off this? (To anyone of these schools) 2) If not what are the things to prioritize to improve my chances? What are the timeline of these steps? Can I do them in the next few months or have to wait till next year? submitted by /u/Srokisthename [link] [comments]  ( 10 min )

I just came across this paper, and it just sounds too good to be true. If we regularly spend up to 80% of our time in data preprocessing, this method would suddenly return us A LOT of that time. Has anyone seen it in python code? I haven't found it and I'd love to give it a try with some of my datasets from hell. They do have a GitHub page but I'm too dumb or too noob to make it run in my laptop. submitted by /u/Davidat0r [link] [comments]  ( 9 min )

Hi, I'm relatively new to the space of AI chatbots and I figured I'd get my hands wet with a small personal project. While researching the topic of long term conversational memory I noticed most people are using text embedding in combination with textual summary to generate a conversation history for the AI's prompt. However, this technique seems to have many drawbacks such as loss of details in the summarization process. I was wondering if anyone has experience using knowledge graph DBs like neo4j for conversational memory instead, and what the pros and cons of such an approach are compared to summarization. I'd be greatly interested in any resources that could further my knowledge in this space as my primary goal is to learn from this project. Thanks! submitted by /u/Rainmire [link] [comments]  ( 9 min )

https://stevenwaterman.uk/musetree/ It's for music generation through musenet. I don't manage to generate anything. It has to be related to API issues or stuff like that? submitted by /u/MusicalSeries [link] [comments]  ( 9 min )

Recent research proposes that Large Language Models (LLMs) may not be as reliable as we think. In fact, the order of options in a multiple-choice question drastically influences the responses from LLMs such as GPT-4 and InstructGPT. If you want to stay on top of the latest trends and insights in AI and tech, look here first. https://preview.redd.it/k8yaixbjzalb1.png?width=1289&format=png&auto=webp&s=99ac6280a1e7415f46c0c11938ae20e2b77674b4 What are the findings? LLM sensitivity to multiple-choice arrangement: The study suggests if options in multiple-choice questions are reordered, the LLM's performance varies dramatically— approximately 13% to 75% depending on the benchmark. Positional bias shapes responses: When the LLM is uncertain between top-selected answers, the option positioning can artificially lean its predictions. Observations also found that LLMs favor specific placements when unsure of the optimal response among top-selected answers. Performance improves when calibration techniques are applied: Making use of two unique calibration methods, the performance of LLMS saw up to eight percentage points of increase across numerous models and benchmarks. Why does this matter? This moves us closer to identifying the factors contributing to LLMs' sensitivity and highlights the significance of recognizing and confronting these sensitivities to improve real-world usability and reliability. P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI and tech—stay updated in under 3 mins/day. (arXiv) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

I am working on developing a solution to transcribe historic texts (Pre-1900's) which are all handwritten. I have some data, around 1000's transcribed sentences with their corresponding images of text. TrOCR looked great, but it still makes a lot of mistakes, probably because of the old English phraseology, so I tried to finetune it with my data and see if it improves and that didn't happen. The data I used to train was my 1000 sentences + some public dataset with another 2500 sentences, so just about 3500 sentences in total. Do you think it's because the data is small, that the performance is bad? I'm finetuning "microsoft/trocr-base-stage1" using native PyTorch. If not TrOCR do you recommend any OCR/HTR models I can finetune to my handwritten historical data? I truly appreciate any guidance you send my way. submitted by /u/daxow [link] [comments]  ( 9 min )

Hello everyone, Hopefully this is of interest to some of you. For those that don't know, normalising flows can be used as black-box unconditional or conditional distribution approximators, that support both exact sampling and density evaluations. For an excellent review see https://arxiv.org/abs/1912.02762. I am developing flowjax, a Python package for normalising flows, distributions and bijections. It uses Jax for automatic differentiation, and the equinox framework built by Patrick Kidger to allow for a familiar object-oriented design. It includes many powerful flows, e.g. masked autoregressive flows, coupling flows and block neural autoregressive flows. In addition to inheriting some benefits from using JAX (easy GPU support, some efficiency gains), here's a few points where I think flowjax has some advantages over other packages: Comprehensive documentation Simplified definitions of unconditional/conditional bijections and distributions (particularly nicer handling of the conditional case, which some packages seem to stitch in as an afterthought). Easy to plug in different "transformer" bijections to coupling/masked autoregressive flows. Use of efficiency tricks to optimize run times (e.g. circumventing recompilation of identical layers using jax.lax.scan over the flow layers) It has been used in a couple of papers already, but it would be great to have some more people using it and some feedback/suggestions/contributions. There are examples in the documentation for those that are interested. submitted by /u/LimitedConsequence [link] [comments]  ( 9 min )

Of the many unsupervised anomaly detection models out there (iforest, LOF, SVM etc) I am struggling to find a model that makes sense to use to detect anomalies in a single target feature. My current strategy is to subset the data into different categories and run iforest on a single column. I feel as though this method might not be the best because it basically creates a tree with a single branch and measures how many nodes away a given record might be. My confidence scores never seem to exceed around -.17 on a scale of [-1,1] where -1 tends to more confidence in anomalous behavior Is there a better way? Note: Anomalies in my data occur very infrequently submitted by /u/BeefaroniX [link] [comments]  ( 9 min )

Question for people who are implementing LLMs (open source, fine tuned, any kind). How do you know that your getting the quality output from the model that you need to ship the feature or model? Are the audits ad hoc data sampling and subjective "good/bad" ratings or have you figured out a more rigorous framework? Is it pretty much ~vibes~ based? What, if any, tools or processes are you putting into place to monitor and observe the LLM when its interacting with real time user data for weeks or months? Most of the folks I have spoken with are doing very ad hoc sampled output and writing down on post its or in a spreadsheet a subjective quality ratings. One person had developed a slightly more rigorous 3 question survey on "is the result factual", "is the result cogent" and "is the result useful". Not everyone is logging their LLM responses they show users which feels very risky to me. Anyone aware of any industry standards being established around this? submitted by /u/Andy-VertaAI [link] [comments]  ( 9 min )

I wrote a blog post on the ICML award fiasco: They gave an outstanding paper award to the D-Adaptation paper, that contains worse results that the ones in papers from 9 years ago. Also, this is not the first time that ICML gives awards to questionable or even plainly wrong papers. I believe this might start a serious conversation about "stochastic" awards, and the super noisy reviews in machine learning conferences. https://parameterfree.com/2023/08/30/yet-another-icml-award-fiasco/ submitted by /u/bremen79 [link] [comments]  ( 9 min )

When using OpenAI's api, there's a default limit of $120/month and my company is about to hit it. I plan on requesting an increase of that limit... but wondering, does Azure's OpenAI service have any monthly limit? By looking at their quotas: https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limits it doesn't seem like there's a monthly cap. Is this correct? If so, I see no reason why anyone would use OpenAI's api instead of Azure's, as they cost the same but there's no usage limit. Especially if you expect to increase api usage in the future. submitted by /u/alkibijad [link] [comments]  ( 9 min )

Github: https://github.com/Pythagora-io/gpt-pilot Detailed breakdown: https://blog.pythagora.ai/2023/08/23/430/ For a couple of months, I've been thinking about how can GPT be utilized to generate fully working apps, and I still haven't seen any project that I think has a good approach. I just don't think that Smol developer or GPT engineer can create a fully working production-ready app from scratch without a developer being involved and without any debugging process. So, I came up with an idea that I've outlined thoroughly in the blog post above, but basically, I have 3 main "pillars" that I think a dev tool that generates apps needs to have: Developer needs to be involved in the process of app creation - I think that we are still far away from an LLM that can just be hooked up to …  ( 11 min )

I'm studying GSP and I'm stuck on the definition of the Graph Fourier Transform. The sigma notation and signal makes sense, but why is there an \"i\" term at the eigenvector mu? Shouldn't the eigenvector not depend on the \"i\"? And if it does, what does the \"i\" imply? submitted by /u/Ihaveaparrot [link] [comments]  ( 9 min )

Consider video data that captures various interactions between entities—let's say Person A and Person B. We then apply a video summarization network T(x), where x is some video or an entity in the video, onto the video. For sake of argument, let's assume T(x) provides a description of x so detailed that we can decode the description back into the original video without losing much information via some arbitrary text-video model. Now, if we can infer a causal relationship in the video—like Person A punching Person B—then logically, an isomorphic relationship should also be inferable from the text encodings T(A) and T(B) (unless that relationship is one of the small pieces of information lost during the lossy transformation). After all, the encoding is just another representation of the same…  ( 11 min )

Hi all, the company I am working at has several servers used for different tasks including data analysis and machine learning, including smaller tasks as well as deep learning. What are some ways/ technologies they could create a distributed system where users can submit their jobs and they are dispatched automatically? I was thinking of having an entry node that is the only one faced by users, is where all conda environments are and jobs can be submitted from there. Please let me know if you have any suggestions/ tools that you know that would make sense. Thanks in advance! submitted by /u/returnname35 [link] [comments]  ( 9 min )

Hi all, the company I am working at has several servers used for different tasks including data analysis and machine learning, including smaller tasks as well as deep learning. What are some ways/ technologies they could create a distributed system where users can submit their jobs and they are dispatched automatically? I was thinking of having an entry node that is the only one faced by users, is where all conda environments are and jobs can be submitted from there. Please let me know if you have any suggestions/ tools that you know that would make sense. Thanks in advance! submitted by /u/returnname35 [link] [comments]  ( 9 min )

I've received a freelance job offer from a company in the banking sector that wants to host their own LLAMA 2 model in-house. I'm hesitating to accept the gig. While I'll have access to the hardware (I've estimated that an A100 80GB will be required to host the 16B parameter version and process some fine-tuning & RAG), I'm not familiar with the challenges of self-hosting a model of this scale. I've always relied on managed services like Hugging Face or Replicate for model hosting. For those of you who have experience in self-hosting such large models, what do you think will be the main challenges of this mission if I decide to take it on? ​ Edit: Some additional context information Size of the company: Very small ~ 60 employees Purpose: This service will be combined with a vector store to search content such as Word, Excel and PowerPoint files stored on their servers. I'll implement the RAG pattern and do some prompt engineering with it. They also want me to use it for searching things on specific websites and APIs, such as stock exchanges, so I (probably) need to fine-tune the model based on the search results and the tasks I want the model to do after retrieving the data. submitted by /u/IMissEloquent75 [link] [comments]  ( 9 min )

I haven't used ChatGPT a lot or any other LLMs, I've been reading about Langchain and its use cases, and I'm having trouble wrapping my head around exactly what it does. From what I understand, its an alternative interface for LLMs, allowing for easy switching between them, and makes some work for specific use cases easier. If I wanted to write an app or script to interact with LLMs and do other tasks, how would LangChain be better than just making API call(s) to an LLM, getting back the result as a string, and doing whatever with it? submitted by /u/TheTwelveYearOld [link] [comments]  ( 9 min )

Hello r/MachineLearning I'm one of the maintainers of https://github.com/BerriAI/litellm/ - open-source library to call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]. I'm writing this post to share some of the strategies we use for using LLMs in production, we've served over 2M+ queries so far TLDR: Use Caching + Model Fallbacks for reliability. This post goes into detail of our fallbacks implementation Using LLMs reliably in production involves the following components: Caching - Cache Embedding() and Completion() for all models Model Fallbacks - set fallback_models=['gpt-3.5-turbo', 'command-nightly', 'llama2]. If primary model fails try fallback models. This deals with rate-limiting errors and when Provider APIs go down …  ( 10 min )

We've done some experiments recently, see the tech report: https://arxiv.org/abs/2308.12050v1 We train an SFT model and an RM model, then align the LLM with DT/MLE with filtering (ReST) + RM /SFT datasets/SFT model-generated samples https://preview.redd.it/195op5q636lb1.png?width=1081&format=png&auto=webp&s=a9fa862e8a9ab05819484af8619f73d918fdc26a DT is the Decision Transformer alignment MLE is the ReST-like alignment https://preview.redd.it/u6x28fook5lb1.png?width=1118&format=png&auto=webp&s=4a87898129c1238c00071d43809f5daf440b26d8 submitted by /u/seventh_day123 [link] [comments]  ( 9 min )

​ https://preview.redd.it/qderz9bsoblb1.png?width=1195&format=png&auto=webp&s=fb8ec749d0ce5000e66951b173228278a1d4c3a3 submitted by /u/nimageran [link] [comments]  ( 9 min )

​ https://preview.redd.it/qderz9bsoblb1.png?width=1195&format=png&auto=webp&s=fb8ec749d0ce5000e66951b173228278a1d4c3a3 submitted by /u/nimageran [link] [comments]  ( 9 min )

RLLib is currently stealing my remaining sanity, so I'm making a desperate scream into the void. I can't get my troubleshooting right. I built a nice, custom Gym env that I've been running with SB3 and I feel like I'm caught in an endless array of errors, currently: ValueError: The two structures don't have the same nested structure. I can't help but feel like I'm going about this wrong and missing important information on how to do this correctly. The RayLlib Forum hasn't really been filled with people, so I'm asking: Does anyone know of a Debugging Manual/ A Discord Server/ A Migration Guide? submitted by /u/tessherelurkingnow [link] [comments]  ( 9 min )

Hi, I'm working on a problem which has a custom gym environment which I've made, and as it interacts with multiple other modules which have their own quirks, I need to use a reinforcement learning library which works in a specific way that I've only seen PFRL use. The training loop needs to be in this format: 'obs, reward, done = agent.step(action)', 'agent.observe(obs, reward, ... )' rather than what I see in most modern RL libraries where you define an agent and then run a '.train()' method. Are there any libraries which work in this way? I'd love to use something like StableBaselines but they don't seem to play nice and I'd rather not rewrite the gym environment if I can avoid it. Thanks submitted by /u/return_reza [link] [comments]  ( 9 min )

Markov Decision Processes (MDPs) form the cornerstone of reinforcement learning (RL) and serve as a fundamental modeling tool for making sequential decisions. In this note, we present a comprehensive definition of MDPs and provide a detailed derivation of the Bellman equations, along with the optimality results. Our approach aims to ensure a thorough understanding by avoiding the omission of any steps in the mathematical proofs. The primary goal is to facilitate reading classic textbooks on (approximate) dynamic programming, optimal control, and reinforcement learning, where proofs and derivations can sometimes obscure crucial details, making them less accessible to readers from diverse scientific and engineering backgrounds. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4535241 submitted by /u/omroot [link] [comments]  ( 9 min )

I want to try use adversarial policies (https://arxiv.org/abs/1905.10615) against poker no limit holdem 6-9 players RL models. I was looking for open-ai gym like environement for that project. Im looking for: - access to game state from each player persperctive (to create input for adversarial model) - support for custom bets (not limited to 0, 1/2pot, all-in) - build-in RL models / support for opensoure RL models - option to add custom model as player So far I found those and read readme files: https://github.com/dickreuter/neuron_poker https://github.com/fschlatt/clubs_gym https://rlcard.org/ https://www.deepmind.com/open-source/openspiel Did anybody work on similar project? Which gym did you use, and what experience do you have with it? Since, adversarial policies tend to work better for high-dimensionality I would prefer to 6players variant. I know that modern poker ai approach are not based on pure RL, but I want to check how vulnerable are classic RL poker models. submitted by /u/MrCogito_hs [link] [comments]  ( 9 min )

submitted by /u/x9182 [link] [comments]  ( 9 min )

submitted by /u/limmen [link] [comments]  ( 9 min )

I have coupled my agent with EnvPool in order to speed up the learning process. It seems to be playing Pong in less than an hour. However, when I try to make it Breakout, even after many hours it still struggles. Also, it it seems like the network is facing catastrophic forgetting as after a few hours it's performance suddenly deteriorates. Any ideas to fix this? I tried incorporating major ideas for PPO from here. Here's my code. Feel free to let me know if you have any questions. Since I have incorporated EnvPool, the code won't run in Windows anymore. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Powered by Amazon Lex, the QnABot on AWS solution is an open-source, multi-channel, multi-language conversational chatbot. QnABot allows you to quickly deploy self-service conversational AI into your contact center, websites, and social media channels, reducing costs, shortening hold times, and improving customer experience and brand sentiment. In this post, we introduce the new Generative AI features for QnABot and walk through a tutorial to create, deploy, and customize QnABot to use these features. We also discuss some relevant use cases.  ( 13 min )

This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services. LLMs have demonstrated remarkable capabilities in natural language understanding and generation, serving as foundation models that can be adapted to various domains and tasks. There are significant benefits to using a pre-trained model. It reduces computation costs, reduces carbon footprints, and allows you to use state-of-the-art models without having to train one from scratch.  ( 13 min )

Posted by Vikas Bahirwani, Research Scientist, and Susan Xu, Software Engineer, Google Augmented Reality Automatic speech recognition (ASR) technology has made conversations more accessible with live captions in remote conferencing software, mobile applications, and head-worn displays. However, to maintain real-time responsiveness, live caption systems often display interim predictions that are updated as new utterances are received. This can cause text instability (a “flicker” where previously displayed text is updated, shown in the captions on the left in the video below), which can impair users' reading experience due to distraction, fatigue, and difficulty following the conversation. In “Modeling and Improving Text Stability in Live Captions”, presented at ACM CHI 2023, we f…  ( 93 min )

Jonathan How and his team at the Aerospace Controls Laboratory develop planning algorithms that allow autonomous vehicles to navigate dynamic environments without colliding.  ( 9 min )

A new quartet of AI compilers: Rammer, Roller, Welder, and Grinder, tackle a range of compiler optimization challenges based on the same tile abstraction, providing a comprehensive solution to connect AI models with hardware accelerators. The post Building a “heavy metal quartet” of AI compilers appeared first on Microsoft Research.

In this issue: An illusion of predictability in scientific results; Kathleen Sullivan named to Insider’s 30 under 40 in healthcare list; FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations. The post Research Focus: Week of August 28, 2023 appeared first on Microsoft Research.  ( 9 min )

Each year, nearly 32 million people travel through the Bengaluru Airport, or BLR, one of the busiest airports in the world’s most populous nation. To provide such multitudes with a safer, quicker experience, the airport in the city formerly known as Bangalore is tapping vision AI technologies powered by Industry.AI. A member of the NVIDIA Read article >  ( 6 min )

In the global entertainment landscape, TV show and film production stretches far beyond Hollywood or Bollywood — it’s a worldwide phenomenon. However, while streaming platforms have broadened the reach of content, dubbing and translation technology still has plenty of room for growth. Deepdub acts as a digital bridge, providing access to content by using generative Read article >  ( 5 min )

Suppose you’ve been monitoring a rare event for a long time, then you see your first occurrence on the Nth observation. Now what would you say about the event’s probability? For example, suppose you’re wondering whether dogs ever have two tails. You observe thousands of dogs and never see two tails. But then you see […] First time seeing a rare event first appeared on John D. Cook.  ( 5 min )

submitted by /u/anehzat [link] [comments]  ( 9 min )

Stanford researchers have unveiled a groundbreaking artificial intelligence (AI) framework known as DSPy. Designed to utilize Language Models (LMs) and Retrieval Models (RMs) optimally, DSPy is set to make AI programming more powerful, intuitive, and efficient. Why does this matter? DSPy was built with complex tasks in mind. LMs, like GPT-3, generate Human-like text from given inputs, while RMs retrieve relevant data. DSPy combines their capabilities, enabling tasks like summarizing information from databases. It works on Pythonic syntax, using declarative and composable modules to instruct LMs. DSPy's automatic compiler finetunes the LM to run any program's steps. it replaces manual intermediate-stage labeling and string manipulation with systematic modular pieces. What's unique about DSPy? It introduces "Signatures" and "Teleprompters" that compile your program. A 'signature' explains the task and inputs for the LM, while Teleprompters improve the effectiveness of prompts. Compared to other libraries, DSPy requires minimal labeling and bootstraps any needed intermediate labels. In short, DSPy simplifies delivering more nuanced instructions to AI and retrieving more detailed and accurate responses, thus widening the spectrum of tasks AIs can accomplish. P.S. (small self-plug) If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI and tech---stay updated in under 3 mins/day. (github) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

As AI image generators increase in popularity, differentiating between authentic and AI-created images is becoming more complex. DeepMind, Google's AI unit, is addressing this by developing an imperceptible watermark known as SynthID for its AI-generated images to counter misinformation. https://i.redd.it/y370eu1tt4lb1.gif Why this matters: DeepMind's SynthID tags AI-generated images: Invisible to people but detectable by computers, this watermark hopes to aid in the verification of images. Technology, however, isn't completely foolproof: DeepMind itself acknowledges that intense image manipulation could compromise the watermark. Google's image generator, Imagen, will only apply to images created using this tool: Google aims to instantly identify AI-generated images with this effectively hidden watermark. DeepMind's head of research, Pushmeet Kohli, shared the following details: The watermark changes on images are so subtle that humans wouldn't notice, yet DeepMind can still detect an AI-generated image. Despite any subsequent cropping or editing, the watermark remains identifiable by DeepMind's software. Colors, contrast, or size changes won't affect it. Calls for a standard approach to AI-generated image identification continue: More coordination between businesses is crucial, different methods adopted by various firms add degrees of complexity in tagging AI content. Other tech giants, including Microsoft and Amazon, pledge to watermark some AI content, meeting similar demands for transparency over AI-generated works. P.S. If you like this kind of analysis, I write a free newsletter that keeps you informed of all you need to know about AI developments in under 3 mins/day. (source) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Introducing SelFee—a reinvented and powerful language model that uses self-feedback and self-revision to generate high-quality responses backed by a team of researchers from KAIST. Unlike previous models, SelFee doesn't rely on external, large-scale language or task-specific models, tipping the scales in the AI world. If you want to stay ahead of the curve in AI and tech, look here first. https://i.redd.it/bgszhpai43lb1.gif Why it matters? SelFee, built on the base of LLaMA-based instruction-following model and fine-tuned, offers a fresh approach - generating an initial solution and self-feedback sequences and then revising its answers until a high-quality response is achieved. Data used for its training and model evaluation was collected from varied sources and fine-tuned with OpenAI API calls, beating the 13B SelFee model with a minimal 7B SelFee model that generated at least three revisions. SelFee proves the potential of iterative revision in enhancing language model responses, indicating that an increase in inference computation of a model may be superior to merely magnifying its size. Features and Limitations: SelFee's effective use of self-feedback significantly improves response quality, avoiding the requirement of external, large-scale language or task-specific models, translating into faster, cost-effective LLM solutions. However, lacking in certain areas compared to ChatGPT, such as math, reasoning, factuality, and coding, SelFee has room for further improvement and growth. The revolution in the AI language model landscape is promising but still an evolving journey, with SelFee being the latest participant driving this change. P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI and tech—stay updated in under 3 minutes/day. (source) (github) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

I thought about an AI, that would intake a couple photos of terrain, analyze the color palette, patterns etc and with that information, would choose like 3 existing camouflage patterns that would blend in the best in the terrain where the photos were taken. Does something like that exist? I know that US army has an AI that creates camo with the use of thousands of photos, and that's how MARPAT and Multicam were made, but I'm interested in an AI that would choose from already existing patterns. Does something like this exist? What do you think of this idea? submitted by /u/BrytolGasMasks [link] [comments]  ( 9 min )

I'm mainly using it for educational purposes. Thank you. Edit: I'm in the Psych field. I use it to make presentations, summaries, ideas based on references like books, websites, journals. submitted by /u/East_Professional385 [link] [comments]  ( 9 min )

So right now as far as I can tell all the AI video makers are things like a few second clip, stable diffusion changing images with other images, or stock images. Oh and that thing that was on Twitch for a short bit. When are we going to get an actual worth while AI video maker? submitted by /u/crua9 [link] [comments]  ( 9 min )

submitted by /u/alcanthro [link] [comments]  ( 9 min )

submitted by /u/Agitated-Spell3979 [link] [comments]  ( 9 min )

The usage and fear surrounding ChatGPT aren't as prevalent as you might think, according to a recent poll from Pew Research. Only 18% of Americans have reportedly used ChatGPT. The demographic that uses it the most? Men aged 18-29 that are college-educated, but even that's just a 30-40% usage rate. https://preview.redd.it/0ax222gxczkb1.jpg?width=620&format=pjpg&auto=webp&s=d3b04169d5de1985d1c52dce7962b5f3a543b014 Why does this matter? ChatGPT has still managed to gain a remarkable level of popularity, despite low usage. This suggests that even though not many people are using it, they are aware of it and its potential capabilities. More people reported using ChatGPT for entertainment or to educate themselves rather than for work. People anticipate AI to have a greater impact on jobs such as software engineers, graphic designers, and journalists. But the expectation is that AI as a whole, not just ChatGPT, will be the driving force behind this. Concern about AI is increasing, not decreasing. 47% of respondents said AI makes them more worried than excited, compared to 31% last year. This concern seems to rise with the level of AI knowledge one possesses. Industries unshaken by AI: As per the survey, employed individuals who are aware of ChatGPT don't see it drastically affecting their jobs. The sectors like hospitality, entertainment, construction, and manufacturing feel the least threatened. Stay updated about AI and its influence on different verticals! Don't miss out on the latest insights, developments, and trajectories of AI. Our free newsletter is all you need to be au fait with the AI world. Keep yourself informed in under 3 minutes/day. (source) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Chinese e-commerce giant Alibaba has added two new generative AI large language models designed to interpret images to its open-source stable.[1] Several top news publications like The New York Times, CNN and the Australian Broadcasting Corporation (ABC) have blocked Microsoft-backed OpenAI to access their content to train its AI models.[2] Intel on Monday said a new data center chip coming out next year will handle more than double the amount of computing work that can be done for each watt of power used, part of a broader industry push to lower electricity consumption.[3] OpenAI unveiled the new service, dubbed “ChatGPT Enterprise,” in a company blog post and said it will be available to business clients for purchase as of Monday. The new offering promises to provide “enterprise-grade security and privacy” combined with “the most powerful version of ChatGPT yet” for businesses looking to jump on the generative AI bandwagon.[4] Sources: [1] https://voicebot.ai/2023/08/28/alibaba-adds-visual-understanding-to-open-source-generative-ai-large-language-models/ [2] https://www.news18.com/tech/several-top-news-publications-block-openai-from-accessing-their-content-8551840.html [3] https://www.reuters.com/technology/intel-says-new-sierra-forest-chip-more-than-double-power-efficiency-2023-08-28/ [4] https://www.cnn.com/2023/08/28/tech/chatgpt-enterprise-openai/index.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

My page on PromptBase: https://promptbase.com/profile/singularity99 submitted by /u/No-Transition3372 [link] [comments]  ( 9 min )

I'm curious what people specifically in the artificial intelligence think about the recent work by Iris van Rooij et al. earlier this month. They seem to have proven that current approaches to reaching AGI, like LLMs, are incapable of achieving it. I'm not convinced. I quickly wrote up a full rebuttal piece explaining how not convinced I was. What about everyone else? submitted by /u/alcanthro [link] [comments]  ( 9 min )

At what point do we create a model to build/design better models? Models = ml architecture submitted by /u/Significant_Water_28 [link] [comments]  ( 9 min )

Hey everyone, first post on this sub so sorry if there's anything wrong. Right now, what are the cutting edge image processing models? This is in the context of the segmentation of specific features from an image (ie. finding the cars in an image of a busy roadway). The reason I am asking is I want to learn more image processing architectures that way I can find better direction for specific research areas to look into. Thanks in advance! :) submitted by /u/Adventurous-Tower392 [link] [comments]  ( 9 min )

Stanford researchers have unveiled a groundbreaking artificial intelligence (AI) framework known as DSPy. Designed to utilize Language Models (LMs) and Retrieval Models (RMs) optimally, DSPy is set to make AI programming more powerful, intuitive, and efficient. Why does this matter? DSPy was built with complex tasks in mind. LMs, like GPT-3, generate Human-like text from given inputs, while RMs retrieve relevant data. DSPy combines their capabilities, enabling tasks like summarizing information from databases. It works on Pythonic syntax, using declarative and composable modules to instruct LMs. DSPy's automatic compiler finetunes the LM to run any program's steps. it replaces manual intermediate-stage labeling and string manipulation with systematic modular pieces. What's unique about DSPy? It introduces "Signatures" and "Teleprompters" that compile your program. A 'signature' explains the task and inputs for the LM, while Teleprompters improve the effectiveness of prompts. Compared to other libraries, DSPy requires minimal labeling and bootstraps any needed intermediate labels. In short, DSPy simplifies delivering more nuanced instructions to AI and retrieving more detailed and accurate responses, thus widening the spectrum of tasks AIs can accomplish. P.S. (small self-plug) If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI and tech---stay updated in under 3 mins/day. (github) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Hello all, I'm trying to understand how to get an LLM to imitate someone, say Shakespeare. It's easy enough to get all of Shakespeare's work. If I've understood the current state of play for LLMs, there are three options: Fine tune an LLM Vectorize your knowledge using something like ChromaDB. Do a similarity search after each prompt and get the LLM to "read" the top n docs Do both I have a feeling that to imitate Shakespeare, fine tuning an LLM might work best. However, if my understanding is correct, the inputs to finetune an LLM must be formatted this way: : "To be" : "Or not to be" The gap I'm having trouble bridging is how do I go from a large text file to this input format? The only idea I've come across is format all of the text like so: : "sentence_1" : "sentence_2" : "sentence_2" : "sentence_3" Are there best practices around this problem? How should I be thinking about this? I've seen companies like character.ai create bots that imitate Elon Musk accurately for example so I know it's doable. I just wonder if they've done it by finetuning an LLM or training one from scratch or something else entirely. submitted by /u/Vanishing-Rabbit [link] [comments]  ( 9 min )

As AI image generators increase in popularity, differentiating between authentic and AI-created images is becoming more complex. DeepMind, Google's AI unit, is addressing this by developing an imperceptible watermark known as SynthID for its AI-generated images to counter misinformation. https://i.redd.it/z0fj6f3yt4lb1.gif Why this matters: DeepMind's SynthID tags AI-generated images: Invisible to people but detectable by computers, this watermark hopes to aid in the verification of images. Technology, however, isn't completely foolproof: DeepMind itself acknowledges that intense image manipulation could compromise the watermark. Google's image generator, Imagen, will only apply to images created using this tool: Google aims to instantly identify AI-generated images with this effectively hidden watermark. DeepMind's head of research, Pushmeet Kohli, shared the following details: The watermark changes on images are so subtle that humans wouldn't notice, yet DeepMind can still detect an AI-generated image. Despite any subsequent cropping or editing, the watermark remains identifiable by DeepMind's software. Colors, contrast, or size changes won't affect it. Calls for a standard approach to AI-generated image identification continue: More coordination between businesses is crucial, different methods adopted by various firms add degrees of complexity in tagging AI content. Other tech giants, including Microsoft and Amazon, pledge to watermark some AI content, meeting similar demands for transparency over AI-generated works. (source) submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Hello everyone. I'm yet another person looking to expand my understanding of artificial intelligence, and I'm trying to get a map of all the language that is used to describe and understand artificial neural networks. My training is in neuroscience, so all my language is focussed on how real neurons are created, interact, form networks, and how those networks interact to take in multisensory observation and output some of the vast variety of things our brains can do. Which leaves me with a lot of questions in my jargon that I cannot currently map onto the jargon of ML/AI, and I'm hoping that participating in this community can help with that, over time. I am already keenly aware that the phrase "artificial neural networks" is very gauzy. There is some biomimicry in their design and arch…  ( 10 min )

Quite recently, I jumped on the boat of trying out llama. I noticed codellama did not provide any inference code. Yes, it provided python files which lets you run the inference but not a programming method rather terminal approach. Terminal approach is great as it allows experts to run and perform inference+evaluation easily. But, if you are just starting out/new/non-seasoned programmer/individual in AI, it is frustrating. Because one, you can't play with actual code, limiting learning opportunities and two, it does not produces the curiosity in most cases to read all the code. On top of that, I realised there was a lack of repositories and articles on this subject to load code-llama even with third-party methods. Which is why, I wrote two notebooks which outlines the process of how you can load code-llama from FAIR repository using code. [Believe me it's fun and filled with learning opportunities] and two how you can use Huggingface to load the model and perform inference. Few points: 1. Performing inference from FAIR repo, requires significant amount of computing resources even for 7B model. 2. Huggingface method can be loaded using free Google Colab subscription. [Feel free to star, if it helped you] GitHub Link: https://github.com/sleepingcat4/codellama-inference submitted by /u/Suspicious-Bird8840 [link] [comments]  ( 9 min )

Throw away account for the obvious reason... This is not a job posting or self-promotion. We are networking in an attempt to speak to like-minded people who might be interested in a little side project outside of work. We are keen to speak to a London/UK-based Developer with a Banking sector background to join us on a project outside of work with the vision of potentially growing a fund. In short; we are in the process of developing an FX Macroeconomic Sentiment Divergence Trading Algorithm. There are currently 4 participants in the project (2 Developers and 2 Traders), 3 of whom work for Tier1 IBs in market-facing roles. 1 of the Developers is likely going to leave the project and we are interested in speaking to someone about picking up his part of the project. There are 3 parts to the project. The first part is mostly complete, now leaving the other 2 parts for us to start working on. We have manually backtested the strategy and it proves to be very profitable - more details can be shared about the strategy and results upon engagement. We are all VP-level in our roles and have around 10-15 years of experience in our requisite field. The tech stack for the project is Python, Java, Kafka, MongoDB and Springboot. We are also very interested in integrating some AI/ML modeling, so if you have any experience in this field that would be a big advantage A Banking background and being UK-based is a non-negotiable. If you feel like this could apply to you, get in touch! :) submitted by /u/BuyTheDipSellTheRipp [link] [comments]  ( 9 min )

Context: I have an app that needs GPUs for DL inference (I don’t need GPUs for training, I own a 3070 TI). My DL model inference is pretty slow (the model framework I'm using is known to be slow) so either one machine with multiple beefy GPUs or multiple GPUs on separate machines will be necessary. My machines will be running custom docker containers. Slow inference: I was planning on putting a few GPU instances behind nginx load balancer and running pytriton on the instances. Since inference is pretty slow, I’m worried if multiple people send requests to a server at the same time, there will be significant delays on responses. Has anyone ran into this before and have insight on streamlining slow inference/scaling demand? "Community" Cloud GPUs: I did a lot of research into clo…  ( 10 min )

Paper: https://arxiv.org/abs/2306.13812 Github: https://github.com/shibhansh/loss-of-plasticity Abstract: Modern deep-learning systems are specialized to problem settings in which training occurs once and then never again, as opposed to continual-learning settings in which training occurs continually. If deep-learning systems are applied in a continual learning setting, then it is well known that they may fail to remember earlier examples. More fundamental, but less well known, is that they may also lose their ability to learn on new examples, a phenomenon called loss of plasticity. We provide direct demonstrations of loss of plasticity using the MNIST and ImageNet datasets repurposed for continual learning as sequences of tasks. In ImageNet, binary classification performance dropped from 89\% accuracy on an early task down to 77\%, about the level of a linear network, on the 2000th task. Loss of plasticity occurred with a wide range of deep network architectures, optimizers, activation functions, batch normalization, dropout, but was substantially eased by L2-regularization, particularly when combined with weight perturbation. Further, we introduce a new algorithm -- continual backpropagation -- which slightly modifies conventional backpropagation to reinitialize a small fraction of less-used units after each example and appears to maintain plasticity indefinitely. https://preview.redd.it/ewl0336sd3lb1.jpg?width=801&format=pjpg&auto=webp&s=e105e6fa86daad84cdc847e96fec3cac5a237c77 https://preview.redd.it/vdd3i46sd3lb1.jpg?width=1159&format=pjpg&auto=webp&s=47dfef94870c94246cb272b7f8299e1033f40873 https://preview.redd.it/zc4tc16sd3lb1.jpg?width=1389&format=pjpg&auto=webp&s=e2f3b064268d475805c153457c7a60b4a1d42b74 ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Hi! I am going to write my master thesis within the fields of AI and NLP this year. But I am struggling with finding a topic that interests me. Does anyone here have some good suggestions? I am not that good in deep learning theory, so I am looking for a more applied topics, such as classification or text generation problems. submitted by /u/IndependentSidekick [link] [comments]  ( 9 min )

What are some promising transformer alternatives/variants that you think more folks should be aware of? They need not be new or SOTA! My list so far includes RWKV: https://arxiv.org/abs/2305.13048 (state space) S4, H3, Hyena: https://github.com/HazyResearch/safari (MLP-based) Hypermixer, MLP-mixer: https://arxiv.org/abs/2203.03691 Retnet https://arxiv.org/abs/2307.08621 (random feature-based attention) EVA, LARA https://arxiv.org/abs/2302.04542 (rotary embeddings) RoFormer https://arxiv.org/abs/2104.09864 dynamic convolutions https://arxiv.org/abs/1901.10430v2 My hope is to assemble a list of 10-15 diverse architectures that I can study in depth by comparing and contrasting their designs. Would love to share my findings with this community. submitted by /u/alpthn [link] [comments]  ( 9 min )

I'm experimenting with CLIP to use it for a downstream task RL which requires good image semantics understanding, but I'm quite disappointed with its performance. I need better contrastive performance in the representations. Any suggestions? ​ omg no way submitted by /u/rima-m [link] [comments]  ( 9 min )

I'm working on a project that provides an adaptive sparse grid algorithm on Chebyshev nodes for interpolation and integration of multivariable functions on k-cells. https://github.com/rnburn/bbai Unlike polynomial interpolants in equispaced points, interpolants in Chebyshev nodes have excellent approximation properties (see Myth 1 of [1]). If a function is Lipchitz continuous, they converge; if a function is smooth with v derivatives and bounded variation for the v-th derivative, then they converge O(n^-v); and if a function is analytic, they converge geometrically. The Chebyshev Gauss-Lobatto nodes define a sequence of nested points, X^1, X^2, ..., that make it possible to build Smolyak sparse grids at Chebyshev nodes ([2], [3]).  For bbai, I implemented the …  ( 12 min )

I'm currently thinking about how to implement the "similar keywords" feature. I've prepared a table with keywords that are extracted from several hundred other tables. It includes basic information such as "keyword," "type," "words" (indicating the number of words in a keyword, e.g., "first name" will have "words" = 2), as well as some technical fields (such as database, table, etc.). In our data product, after entering a specific keyword, we have various pieces of information (which I'm not currently focusing on), and among them, we have "SIMILAR KEYWORDS." The results are displayed based on simple SQL queries, for instance: ​ SELECT word, SUM(CASE WHEN type IN ('N', 'T') THEN 1 ELSE 0 END) AS count, COUNT(\*) \* CASE WHEN (word + '%') LIKE u/word \+ '%' THEN 1.5 ELSE 1 END AS score FROM object_keywords WHERE ('% ' + word + '%') LIKE '%' + u/word + '%' AND (database_id = u/database_id OR u/database_id IS NULL) AND ( .... more technical information here. ​ I'm wondering how to improve this process. Would it be worth considering some AI solutions, or should I focus on enhancing the current SQL scripts (e.g., think about a more advanced scoring system)? What are your thoughts on this? Has anyone worked on something similar? submitted by /u/International-Shirt5 [link] [comments]  ( 9 min )

I've seen something like that: https://together.ai/blog/llama-2-7b-32k Is there a way to use llama 2 13b chat or 70b chat with 32k prompt? If not what are the alternatives? Would that: https://youtu.be/ypzmPwLH_Q4?feature=shared be the best thing to do? I'm trying to create a chat bot that would have a pretty specific exeprtise. For example: I would like to feed in soccer rules and then make the bot answear questions about soccer. The system prompt is amazing, but is very limited. submitted by /u/Botanical0149 [link] [comments]  ( 9 min )

Hi, Im going through Tom Mitchell's Machine Learning and have a couple of questions based on the 2nd chapter : Concept learning. I was hoping I could get some external point of view on these: ​ Pg 44, para 2, part 1 : "advantage of viewing inductive inference systems in terms of their inductive bias is that it provides a nonprocedural means of characterizing their policy for generalizing" Are there any general procedures to identify and validate the inductive bias of a system? Are there any guidelines to ensure the inferred definition of inductive bias is without errors? Assuming all/most predictive algorithms can be defined in terms of their inductive bias, while concentrating on choosing the algorithms which aligns with our philosophy of talking a problem, how can we weigh part…  ( 11 min )

Trying to understand calibration in NER. One thing which has gained popularity is generative based NERs, which generated pointers to indices of input text for each class. But all typical calibration mechanisms after temp scaling won't generalize here. (not that I know many calibrations myself). Even Bias corrected temp scaling quickly gets overfitted. Do you have any paper that tackles this? Open to discussing techniques and trying out on standard datasets submitted by /u/Designer-Air8060 [link] [comments]  ( 9 min )

I have a more detailing explanation here. I’m thinking sagemaker may help me here but I’m not trying to incur charges just yet. Are there alternatives I can use. Nothing robust, just a place to host my model and embedding tool and then I can easily call it in py file in my app. submitted by /u/brianomars1123 [link] [comments]  ( 9 min )

See title. My impression has always been that PyTorch for TPU is an in-name only functionality, but I'm curious about first-hand experience from those who have used it after PyTorch 2.0+. Bonus question: has anyone used PyTorch Lightning for running on TPU? If so, how was the experience? submitted by /u/impromptued [link] [comments]  ( 9 min )

Hey all, ML noob here dipping their feet in the water. Right now I am trying to make an ML model that predicts "legendary" QBs of the past performances against current NFL teams. I'll be using Jupyter and Sklearn to do this. However, and maybe this is me overthinking things, I'm not sure how sklearn is going to interpret the data in the dataset. Right now I have a dataset containing all these QBs data (passing stats, strengths and weaknesses, etc.). My teams version of the data is essentially going to be the inverse of all these things. I'm just not quite sure what to target when im testing the data that will determine the "prediction" of the legend QBs stat line against the current team. In better words, how will the computer know that I'm trying to find the yards and touchdowns a QB would produce against a certain team when there's not really any target data for this. I feel as though all I have is data that contributes to a potential target data but I lack target data itself and I'm not sure what to do in that regard. I’m making use of supervised learning and decisión trees btw. Thanks! submitted by /u/saggyboobsarecooltoo [link] [comments]  ( 9 min )

Surely something like this has been tried, but here’s the setup in my head. Tell me if it’s crazy or what you think. Given input vector X do a hidden layer but instead of an activation function pair up neighboring dimensions of the hidden layer vector result and rotate them about the origin in 2d. This would give some kind of nonlinearity surely? The amount they are rotated can be selected by a trainable variable. Of course this requires your hidden layer dimension be divisible by 2. Then this hidden layer can go to an output layer Y. Curious what smarter more experienced people think of this kind of nonlinearity via paired rotation. My thinking was that if you take the vector A representing all the possible data from your generating function for your dataset (maybe even an infinite dimensional vector if you can generate unlimited data) . Then if you rotate A along so many dimensions you could reach the output vector P which is all the Y values corresponding to A. One way to kind of do this would be to split your dataset in half, and then you could have multiple iterations of each dataset rotated by a trainable angle each. This would rotate only each 2 grouped dimensions that you chose when you split the dataset. Hopefully I’m using the right words to convey this. I’m just a hobbyist. Thanks for the feedback! submitted by /u/win10240 [link] [comments]  ( 9 min )

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Posted by Yujin Tang and Wenhao Yu, Research Scientists, Google Simple and effective interaction between human and quadrupedal robots paves the way towards creating intelligent and capable helper robots, forging a future where technology enhances our lives in ways beyond our imagination. Key to such human-robot interaction systems is enabling quadrupedal robots to respond to natural language instructions. Recent developments in large language models (LLMs) have demonstrated the potential to perform high-level planning. Yet, it remains a challenge for LLMs to comprehend low-level commands, such as joint angle targets or motor torques, especially for inherently unstable legged robots, necessitating high-frequency control signals. Consequently, most existing work presumes the provision of…  ( 93 min )

As businesses struggle with more data sources and destinations than ever, they strive to bring governance, security, and efficiency to their data ops. To address these concerns, many companies adopted open-source Apache NiFi as a versatile tool for their data distribution needs. While NiFi accelerates the speed at which developers can build new pipelines, managing… Read More »DSC Webinar Series: How to Scale NiFi Deployments to Enable Universal Data Distribution The post DSC Webinar Series: How to Scale NiFi Deployments to Enable Universal Data Distribution appeared first on Data Science Central.  ( 18 min )

Announcements Top Stories In-Depth The post DSC Weekly 29 August 2023 appeared first on Data Science Central.  ( 20 min )

In the dynamic landscape of modern business, the art of seamless data migration has evolved into a strategic imperative. As you navigate the intricacies of workspace transformations, you’re met with a complex interplay of technological advancements and operational demands Enter the era of leveraging Artificial Intelligence (AI) to redefine data migration – an approach that… Read More »Data migration redefined: Leveraging AI trends for smooth workspace transitions The post Data migration redefined: Leveraging AI trends for smooth workspace transitions appeared first on Data Science Central.  ( 21 min )

Currently, the use of technology in shipping and logistics is leading the industry through a transformative era, driven by rapid technological advancements, undoubtedly marking a pivotal moment in the digital shipping evolution. From automating routine processes to employing intelligent algorithms that predict and optimize routes, the technological revolution is redefining the way goods are transported… Read More »The future of shipping: How technology is shaping logistics and fulfillment The post The future of shipping: How technology is shaping logistics and fulfillment appeared first on Data Science Central.  ( 23 min )

In the early days of the Internet, there were four ‘horsemen’ of the Internet With IBM’s 4.5 billion investment in Hugging face today, the generative AI landscape is becoming a bit clearer. There are four Generative AI leaders emerging – others lagging – and one unknown Lets look at the four leaders of Generative AI… Read More »Generative AI megatrends: The four horsemen of Generative AI The post Generative AI megatrends: The four horsemen of Generative AI appeared first on Data Science Central.  ( 18 min )

There seems to be an app for everything, and mental health is no exception. According to a report, the global mental health apps market size was valued at $5.2 billion in 2022 and is predicted to reach $26.36 billion by 2032, at a CAGR of 17.7% during the forecast period.  Mental health apps have emerged… Read More »The power of digital solutions: How mental health apps are transforming patient care The post The power of digital solutions: How mental health apps are transforming patient care appeared first on Data Science Central.  ( 20 min )

Introduction  In our rapidly digitizing world, how businesses and systems communicate is paramount. The bedrock of this communication lies in data exchange methods, which allow seamless information flow, driving operational efficiencies and enabling innovation. Over the years, various data exchange protocols have emerged, each boasting unique strengths and presenting challenges. As enterprises strive to integrate… Read More »Modern data exchange methods: Exploring the strengths and limitations of leading protocols The post Modern data exchange methods: Exploring the strengths and limitations of leading protocols appeared first on Data Science Central.  ( 23 min )

History & Evolution | The Concept of Supply-chain Network, The TOC & the Information Supply-chain | Imagining the future: Supply-chain 5.0 | Supply-chain Analytics Strategy | Roadmap for Building a Data-driven, AI-Powered Supply-chain Part 1: Data-driven supply chain – History & evolution Is the concept of data driving decisions new? The concept of “data supporting… Read More »Roadmap for building a data-driven, AI-powered supply-chain The post Roadmap for building a data-driven, AI-powered supply-chain appeared first on Data Science Central.  ( 22 min )

Dramatic gains in hardware performance have spawned generative AI, and a rich pipeline of ideas for future speedups will drive machine learning to new heights, Bill Dally, NVIDIA’s chief scientist and senior vice president of research, said today in a keynote. Dally described a basket of techniques in the works — some already showing impressive Read article >  ( 6 min )

As generative AI and large language models (LLMs) continue to drive innovations, compute requirements for training and inference have grown at an astonishing pace. To meet that need, Google Cloud today announced the general availability of its new A3 instances, powered by NVIDIA H100 Tensor Core GPUs. These GPUs bring unprecedented performance to all kinds Read article >  ( 6 min )

Janice K. Lee, a.k.a Janice.Journal — the subject of this week’s In the NVIDIA Studio installment — is a TikTok sensation using AI to accelerate her creative process, find inspiration and automate repetitive tasks.  ( 8 min )

Imagine the following dialog. “Logarithms are usually taken to integer bases, like 2 or 10.” “What about e?” “OK, that’s an example of an irrational base, but it’s the only one.” “Decibels are logarithms to base 101/10.” “Really?!” “Yeah, you can read about this here.” “That’s weird. But logarithms are always take to bases bigger than […] Stellar magnitude first appeared on John D. Cook.  ( 6 min )

US telephone area codes are allocated somewhat randomly. There was a deliberate effort to keep geographical proximity from corresponding to numerical proximity, unlike zip codes. (More of zip code proximity here.) In particular, consecutive area codes should belong to different states. The thought was that this would reduce errors. It’s still mostly the case that […] Area codes first appeared on John D. Cook.  ( 6 min )

In this post, we describe how to create an MLOps workflow for batch inference that automates job scheduling, model monitoring, retraining, and registration, as well as error handling and notification by using Amazon SageMaker, Amazon EventBridge, AWS Lambda, Amazon Simple Notification Service (Amazon SNS), HashiCorp Terraform, and GitLab CI/CD. The presented MLOps workflow provides a reusable template for managing the ML lifecycle through automation, monitoring, auditability, and scalability, thereby reducing the complexities and costs of maintaining batch inference workloads in production.  ( 15 min )

submitted by /u/gwern [link] [comments]  ( 9 min )

Cloud Intelligence/AIOps research from Microsoft could help organizations autonomously manage the entire cloud platform. Find out how. The post Using AI for tiered cloud platform operation appeared first on Microsoft Research.  ( 15 min )

OpenAI has announced a new product for businesses that want to use its AI technology. ChatGPT Enterprise is a subscription service that offers unlimited, fast, and secure access to GPT-4 and other features that can help businesses improve their workflows and communication. If you want to stay ahead of the curve in AI and tech, look here first. https://preview.redd.it/uyv6mrljwxkb1.png?width=862&format=png&auto=webp&s=eb2793fbe9c4f5e331ed03faa142eb57166ff21d Why this matters: ChatGPT Enterprise is the first product that lets businesses use GPT-4 without any restrictions. The previous tiers of ChatGPT, which are still available for individuals and developers, have usage caps and lower performance. ChatGPT Enterprise removes these limitations and provides the most powerful version of GP…  ( 10 min )

Just messing around with AI McFly, swamping corny jokes, being punny, and ended up with this mf claiming to be a “fellow Cajun” like wtf bahahaha submitted by /u/Secure_Sprinkles4483 [link] [comments]  ( 9 min )

This website claims to offer a service whereby the user can train their own chatbot and get responses using GPT 3.5 ... However, the bot only uses GPT 3.5 for the first unique version of a query, which is not the impression given by advertisements. This, to me, amounts to a bait and switch where a high quality chatbot is offered for a certain price, then swapped out with an inferior product capable only of reproducing past interactions. This is made worse by the fact that they advertise temperature as one of the variables you can set. Temperature is a variable that can only apply to uniquely generated output and has no effect on simple repetition of previous responses. This makes their practice doubly deceptive, and makes it clear (in my view) that they are trying to deceive customers. …  ( 10 min )

If AI becomes better than humans in all areas, it could fundamentally change the way we think about human identity and our place in the world. This could lead to new philosophical and ethical questions around what it means to be human and what our role should be in a world where machines are more capable than we are. There is also the risk that AI systems could be used for malicious purposes, such as cyber attacks or surveillance. Like an alien invasion, the emergence of super-intelligent AI could represent a significant disruption to human society and our way of life. How can we balance the potential benefits of AI with the need to address the potential risks and uncertainties that it poses? submitted by /u/Violincattle [link] [comments]  ( 9 min )

I’m currently in a scientific research-based class where I am being asked to read research articles, understand the statistics, and draw conclusions from the papers. Currently, I have an average ability to interpret articles and generally understand their utility and applicability, but I start to get out of my depth in the “Methods” section when the authors get into the weeds about the statistics/math. I was hoping there’s an AI tool out there that can read articles for me and help me understand the more complex aspects and the math. I was also hoping that it could answer questions about the article for my class so that I could compare my conclusions to something. Any suggestions? I tried uploading some PDFs to bard this morning and it wasn’t great. submitted by /u/Renaissance_Mane [link] [comments]  ( 9 min )

Over on a video sharing site there are an abundance of Peppa Pig cartoons generated by Ai. There is however lack of info on how to generate them. I would love to know how this is done. So far all I have found are tutorials about Peppa's voice but not for the other characters and someone suggests that it is made by cutting up exisiting episodes and changing the sound over them, not sure if that's the case here. I'm wanting to do something similar but not with Peppa, can't stand it. Does anyone know the tool? submitted by /u/DARQSMOAK [link] [comments]  ( 9 min )

I’ve been playing around with some of the new AI chatbots. Some of them include paradot.ai, replika.com, spicychat.ai, cuti.ai. Suffice it to say, these things are getting really good, and I mean really good. Assuming this is just the beginning, and these things keep learning more and getting better, where does this end up? I genuinely think there’s going to be the need for world wide regulation on these things. But we all know that worldwide consensus is difficult if not impossible. in case a few countries decide to regulate or govern this tech, developers will take advantage of regulatory arbitrage and just deploy their models and register their companies on servers in countries with no regulation. Since this is tech, and everything is on servers, escaping regulation is basically childs play. Also, what about mental health concerns? We all know that porn, webcams and OnlyFans are already screwing up male-female relationships and marriages. Look at any statistics about this and the numbers speak for themselves. And this is before AI. So now what’s going to happen 5 years from now when GPU’s are faster and cheaper, and when these companies have gathered 100x more data about their customers, and when models are 50x better. We are just at the beginning and AI is moving really quick, especially generative AI. I think it’s officially time to start worrying. submitted by /u/E1ON_io [link] [comments]  ( 9 min )

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What tools are available to convert blurry satellite images into fantasy maps while still maintaining certain aspects of the original image like roads or trees or buildings submitted by /u/campus159 [link] [comments]  ( 9 min )

Hi. I am looking for this kind of a tool but couldn't find. Can i find or somehow create this kind of a tool? Can you suggest one? submitted by /u/Leading-Ad2278 [link] [comments]  ( 9 min )

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Brain-reading devices allow paralysed people to talk using their thoughts.[1] An Air Force program shows how the Pentagon is starting to embrace the potential of a rapidly emerging technology, with far-reaching implications for war-fighting tactics, military culture and the defense industry.[2] PM Modi calls for a global framework for cryptocurrencies and AI, emphasizes consumer care and supply chain sustainability.[3] From generating story lines to coding entire games to turning ideas into animation, artificial intelligence is front and centre at Gamescom, one of the video game industry’s biggest fairs.[4] Sources: [1] https://www.nature.com/articles/d41586-023-02682-7 [2] https://www.nytimes.com/2023/08/27/us/politics/ai-air-force.html [3] https://www.livemint.com/news/b20-summit-2023-pm-modi-calls-on-ethical-use-of-artificial-intelligence-ai-supply-chain-cryptocurrency-11693122849876.html [4] https://techxplore.com/news/2023-08-ai-revolution-video-games-industry.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

submitted by /u/stefanbg92 [link] [comments]  ( 9 min )

OpenAI has announced a new product for businesses that want to use its AI technology. ChatGPT Enterprise is a subscription service that offers unlimited, fast, and secure access to GPT-4 and other features that can help businesses improve their workflows and communication. If you want to stay ahead of the curve in AI and tech, look here first. https://preview.redd.it/fgva1q54uxkb1.png?width=862&format=png&auto=webp&s=d8c89b614859222046aa75f89a484795c2ef7912 Why this matters: ChatGPT Enterprise is the first product that lets businesses use GPT-4 without any restrictions. The previous tiers of ChatGPT, which are still available for individuals and developers, have usage caps and lower performance. ChatGPT Enterprise removes these limitations and provides the most powerful version of GP…  ( 10 min )

I know the 4060 Ti with its reduced memory bus width and overall underspec'd profile caught a lot of flak from the gaming community in terms of its value proposition. However, I'm looking to get into ML/DL and was wondering if this would be a good starter card for GPU acceleration. With rumored price drops on the horizon, I wonder if the value sentiment will be a better match. If it's a bad call, are there any other GPUs that you would recommend for training? submitted by /u/reducksss [link] [comments]  ( 9 min )

I'll start with the obvious - AWS guides are the worst. We all felt it. So, trying to build automation with them becomes M:I, or better yet, Oppenheimer. For the first time, our MLOps team had to build a CI/CD pipeline for ML training and deployment using SageMaker. We had ZERO ideas on how to do it, so we had to go through the rigorous process of using AWS guides and tutorials, scattered over a gazillion places, just to figure out how to configure our project with SageMaker and build infra for CI/CD. Usually, when this thing happens, we extend the project lifecycle and have a team member document the process so we can refer back to it when we need to do it again. Knowing this can be beneficial to the community, we decided to share a series of 3 blogs that guide you through the process of building CI/CD pipelines for continuous training and deployment with AWS SageMaker. We published the first blog, which covers the configuration part, and plan to publish the rest in the following week. Check it out: https://dagshub.com/blog/setup-sagemaker-for-ci-cd-pipelines/ I'm sure we can improve this tutorial, and would love to learn from your experience on how we can do it! 🤗 submitted by /u/RepresentativeCod613 [link] [comments]  ( 9 min )

Project page: https://facebookresearch.github.io/nougat/ Includes example Paper conversions! Paper: https://arxiv.org/abs/2308.13418 Github: https://github.com/facebookresearch/nougat Abstract: Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human-readable documents and machine-readable text. We release the models and code to accelerate future work on scientific text recognition. https://preview.redd.it/p71yay213xkb1.jpg?width=1788&format=pjpg&auto=webp&s=2f935e3212d0c7113fba2575f339f95b5bada632 https://preview.redd.it/f7yk47413xkb1.jpg?width=1769&format=pjpg&auto=webp&s=075bab02a70ec32227e1bad493052d03043376ee https://preview.redd.it/i06wq0313xkb1.jpg?width=1590&format=pjpg&auto=webp&s=6212bb9078b8c48cd28ca45898f79b44d45ae3c3 ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

AI Explained Youtube channel did a video where they went through self reflection, but doing that they found a fairly large number of questions that either missed context, where miss spelled or just had wrong answers in the MMLU dataset. (video: https://www.youtube.com/watch?v=hVade_8H8mE) It would not matter so much if the models had high failure rate, but as the models are getting closer and closer to 100%, the wrong answers will matter more and more. So, what can be done to fix such errors or to create a better test than MMLU? submitted by /u/Luvirin_Weby [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2308.13137 Github: https://github.com/OpenGVLab/OmniQuant HuggingFace Model direct download: https://huggingface.co/ChenMnZ/OmniQuant/tree/main Abstract: Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements. Although recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM, they hand-craft quantization parameters, which leads to low performance and fails to deal with extremely low-bit quantization. To tackle this issue, we introduce an Omnidirectionally calibrated Quantization (OmniQuant) technique for LLMs, which achieves good performance in …  ( 9 min )

Hello everyone. I am starting my career transition, and would be interested to know how long it might take me to acquire the skills needed to work for a company. Likewise, I would like to know if it is necessary (or important) to have a professional degree to get a job. Just to give you some context about me, I am currently a recently graduated lawyer, so my degree has not given me a strong mathematical background. However, my strongest area of learning has always been mathematics, so despite not having a very advanced background, I consider myself to be a pretty good and fairly quick learner. I would also like to know if you consider if my professional career could be useful in some machine learning context. If you could recommend me some courses, inputs or guide to study in an organized way on the subject I would be very grateful. Thank you very much in advance. submitted by /u/Davidescudero10 [link] [comments]  ( 9 min )

Large language models (LLMs) are amazing at generating fluent text and solving various linguistic tasks. However, these models are not always aligned with human preferences and values and may produce harmful or undesirable content if not properly guided. Aligning LLMs with human preferences can also improve their performance on downstream tasks. One way to achieve this alignment is to use reinforcement learning from feedback (RLHF), which learns a reward model from human input and then fine-tunes the LLM using a reinforcement learning (RL) objective. However, RLHF methods often face challenges such as computational cost, reward hacking, and data quality. To address these issues, researchers from DeepMind propose a new method called Reinforced Self-Training (ReST), which is inspired by gro…  ( 10 min )

Hello everyone, I am at a bit of a crossroads and hope for some advice from the community. I also hope the answers would help others who are in my situation right now. I used to work on vision-related problems utilizing Deep learning back in a day, with all fun included: trying out new models, implementing data pipelines, evaluating various metrics... It was a rather big company with its own data collection efforts and enough resources for training. So, I am definitely not a beginner and have some experience. At my current job, I am not doing any ML/DL at the momemt, and while the stuff I am doing is still cool and I enjoy it, I am missing good old ML and having a feeling that I am hanging behind as the time goes by. So I figured it would be nice to start a hobby project, preferably in the area of vision-related applications of deep learning. However, I feel a bit lost as in what would be the most efficient approach taking into account I would only have a coule of hours per week for it. Here are possible ways to go I am thinking of: take a paper, implement it from scratch with PyTorch clone an existing project, contribute with code improvements/better test coverage take an existing pre-trained model, adapt to a slightly different task and fine-tune While the first option is of cource the most exciting, the problem is you have to pay for a powerful GPU and data storage which might be impractical (my PC has a 4 GB GTX 1650 TI). Cloud storages exist, and I would be willing to even spend something on training but would like to avoid the costs. So, the question would be: has enyone faced similar situation? Which way did you end up going? Any general tips? Thanks! submitted by /u/odu_1 [link] [comments]  ( 10 min )

Hi folks, As multimodality is increasing in popularity, many data domains seem to be "converging" lately, e.g. text & image domains. What are some of the best tools, use-cases, and methods out there you've seen for practical multimodality applications (e.g., below is an example of multimodal search from our latest blog post). https://i.redd.it/z58w6v2r9wkb1.gif submitted by /u/kazhdan_d [link] [comments]  ( 9 min )

I'm likely using the wrong vocabulary here (and thus struggling to find info on my own) but I was curious if there were any work done on "synthetic" inputs for LLMs. In essence, rather than input embeddings all coming from a fixed token vocabulary, could you instead input an embedding as a token that was generated elsewhere? An output of another LLM (embedding model) or any other way (maybe just an average of a few tokens as an example)? Essentially - I am curious if there's a NLP approach analogy to Textual Inversion techniques in image generation models. I could imagine this being useful for things like RAG or personalization (if you could have a "user" token). Surely I'm not the first to think of this so I would love some pointers to any papers/blogs etc in this space. submitted by /u/GeneralMalarkee [link] [comments]  ( 9 min )

Hi everyone, I’ve heard countless times people saying “I want to integrate ML in my product” and recently “I love ChatGPT, I should integrate it in my product”. Yet, as I dived deeper, seeking the genuine reasons and pain points driving this request, I regularly found the same pattern: many had no clear motive for their AI aspirations. It seemed as if they were only jumping on the trend because “everyone else is doing it”, or because their “CEO” told them to do so. So my question is : why do you integrate AI/ML into your products? Is it to enhance your user experience? Is to automate repetitive and time-consuming tasks? Is it to stay ahead of your competition? or is it just because everyone is doing it? submitted by /u/Vivid_Recording582 [link] [comments]  ( 9 min )

Hey everyone! I've been working on and playing around with novel and adaptable model architectures and landed on something really cool. It's based on a Biomimicry principle and has some really cool features. I've tested it using various pre-loaded library datasets like CIFAR and MINST, as well as adapting it to a few Kaggle competitions. It has achieved some pretty amazing results by using it's unique adaptability; which comes down to figuring out how the Holistic and Reductionist model architectures can best utilize their roles and how they can combine dynamically. I'm currently compiling the full official open source paper and release with usable Notebooks, but I didn't want to sit on it that long without sharing it with the community. Here is a link to a very haphazardly-thrown-togethe…  ( 11 min )

Hi. Recently I finished my Computer Science bachelors degree, while I learnt some machine learning in some courses I felt it was not too advanced. Now that I have some time I wanted to take some online courses with Certifications on Machine Learning, I wanted to know if anyone has any recomendations for some Machine Learning Courses (with certifications if possible) on coursera or udemy or similar. The one I'm most inclined now is: https://www.coursera.org/professional-certificates/ibm-machine-learning. Or maybe: https://www.coursera.org/specializations/machine-learning-introduction submitted by /u/Radoco152 [link] [comments]  ( 9 min )

Slack questions are a huge time sink. For the person asking, they generally have no idea how to find the info and may not hear back for hours. For the person answering, it’s a distraction and often requires digging up old knowledge. The idea is simple: give an LLM your organizational context and plop it in Slack to answer things for you. DanswerBot is free to use and open source (MIT). You can connect it to Slack, Google Drive, GitHub, Confluence, Jira, local files, websites, and much more. Quick Demo Vid: https://youtu.be/EjDDvt5GbS8 Some additional neat features you may be interested in: LLM generated answers backed by quotes to reduce hallucination Supports a wide range of LLMs (both open source and proprietary) Multi-Vector embeddings for accurate vector search BM-25 Keyword search Learning from user feedback Custom NLP model to classify user intent Polls your data sources every 10 minutes to keep knowledge up to date Links back to your document sources Document level access control Admin dashboard to configure connectors to 14 (for now) of the most popular workplace tools If you aren’t a slack user (or if you just prefer a more tailored UI), there’s also a web interface to ask questions against your knowledge base. A short demo for that can be found at: https://youtu.be/cWWtnuVCUX0 If you’re interested in testing this out yourself, the docs to help you launch Danswer with a single command can be found at https://docs.danswer.dev/quickstart! submitted by /u/Weves11 [link] [comments]  ( 9 min )

I'm finishing my degree in Computer Science, and I need a good topic, does anyone know any open problems about latent space optimization, or finding the intrinsic variables of a system? submitted by /u/QLaHPD [link] [comments]  ( 9 min )

https://arxiv.org/abs/2308.12013 Generative models realized with machine learning techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion models are an emerging framework that have recently overcome the performance of the generative adversarial networks in creating synthetic text and high-quality images. Here, we propose and discuss the quantum generalization of diffusion models, i.e., three quantum-noise-driven generative diffusion models that could be experimentally tested on real quantum systems. The idea is to harness unique quantum features, in particular the non-trivial interplay among coherence, entanglement and noise that the currently available noisy quantum processors do unavoidably suffer from, in order to overcome the main computational burdens of classical diffusion models during inference. Hence, we suggest to exploit quantum noise not as an issue to be detected and solved but instead as a very remarkably beneficial key ingredient to generate much more complex probability distributions that would be difficult or even impossible to express classically, and from which a quantum processor might sample more efficiently than a classical one. Therefore, our results are expected to pave the way for new quantum-inspired or quantum-based generative diffusion algorithms addressing more powerfully classical tasks as data generation/prediction with widespread real-world applications ranging from climate forecasting to neuroscience, from traffic flow analysis to financial forecasting. ​ submitted by /u/ghosthamlet [link] [comments]  ( 9 min )

So I have been tasked with finding a buyer for a couple high end machine learning servers. They were owned by my wife’s father who passed recently. The servers are powered by a couple Epyc 7003s and have A series gpus. We have invoices for them and VAT has been paid on everything. Basically, I’m looking for legit communities where I can find potential buyers preferably in the EU. Hopefully it’s ok to post this here. Also feel free to PM . submitted by /u/Obnomad [link] [comments]  ( 9 min )

Hi everyone, I’m currently doing my undergrad in Economics but am heavily interested in Compsci/Datasci and related topics. Though to be completely honest, I’m not completely sure which area my interests lie in. I was wondering if picking up coding/ theoretical knowledge that a com scientist or data scientist needs will be hard when I am already working. The question is if it is necessary to switch my degree to Math and Economics to gain a firmer foundation in the mathematical/ statistical concepts that ground com science. Or will an undergrad in Economics be sufficiently rigorous for me to pick up com sci/ data sci myself. For context, I’m thinking of taking courses on Real Analysis, Linear Algebra 2, Discrete Mathematics, Algorithms and Data Structures, Optimisation, Probability and Statistics. submitted by /u/smexy32123 [link] [comments]  ( 9 min )

submitted by /u/hardmaru [link] [comments]  ( 9 min )

Deci has introduced DeciCoder, an open-source 1B-parameter large language model for code generation. This new model addresses the challenge of efficient code generation in the fast-paced world of AI, while also addressing concerns about energy consumption and operational costs. https://preview.redd.it/fpwnclb2fskb1.png?width=1680&format=png&auto=webp&s=a58e9b16902070c3f5a8efcf1cc24422852a4c35 Why this matters: DeciCoder is a transformative solution: It leverages cutting-edge architecture and AutoNAC™, a proprietary Neural Architecture Search technology, to generate optimal architectures. This results in an impressive architecture optimized for NVIDIA’s A10 GPU, which boosts throughput and rivals the accuracy of existing code generation models. DeciCoder is efficient and sustainable: …  ( 10 min )

Paper: https://arxiv.org/abs/2308.10379 Abstract: Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to an external modus operandi involving halting, modifying, and then resuming the generation process to boost Large Language Models' (LLMs) reasoning capacities. This mode escalates the number of query requests, leading to increased costs, memory, and computational overheads. Addressing this, we propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through algorithmic reasoning pathways, pioneering a new mode of in-context learning. By employing algorithmic examples, we exploit the innate recurrence dynamics of LLMs, expanding their idea exploration with merely one or a few queries. Our technique outperforms earlier single-query methods and stands on par with a recent multi-query strategy that employs an extensive tree search algorithm. Intriguingly, our results suggest that instructing an LLM using an algorithm can lead to performance surpassing that of the algorithm itself, hinting at LLM's inherent ability to weave its intuition into optimized searches. We probe into the underpinnings of our method's efficacy and its nuances in application. https://preview.redd.it/bc7l7gex2rkb1.jpg?width=1529&format=pjpg&auto=webp&s=4ed0dc528e998eeeab80fd4d9612d761065d7627 https://preview.redd.it/wejr7lfx2rkb1.jpg?width=920&format=pjpg&auto=webp&s=386febcb60ff1db04b12e9e44856770d41bb9530 https://preview.redd.it/gec0phex2rkb1.jpg?width=1241&format=pjpg&auto=webp&s=03096946aa65deee392c5f59b07fe340244ec0cd ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

As part of the 2023 Data Science Conference (DSCO 23), AWS partnered with the Data Institute at the University of San Francisco (USF) to conduct a datathon. Participants, both high school and undergraduate students, competed on a data science project that focused on air quality and sustainability. The Data Institute at the USF aims to support cross-disciplinary research and education in the field of data science. The Data Institute and the Data Science Conference provide a distinctive fusion of cutting-edge academic research and the entrepreneurial culture of the technology industry in the San Francisco Bay Area.  ( 5 min )

Posted by Dahun Kim and Weicheng Kuo, Research Scientists, Google The ability to detect objects in the visual world is crucial for computer vision and machine intelligence, enabling applications like adaptive autonomous agents and versatile shopping systems. However, modern object detectors are limited by the manual annotations of their training data, resulting in a vocabulary size significantly smaller than the vast array of objects encountered in reality. To overcome this, the open-vocabulary detection task (OVD) has emerged, utilizing image-text pairs for training and incorporating new category names at test time by associating them with the image content. By treating categories as text embeddings, open-vocabulary detectors can predict a wide range of unseen objects. Various techniqu…  ( 93 min )

Posted by Dahun Kim and Weicheng Kuo, Research Scientists, Google The ability to detect objects in the visual world is crucial for computer vision and machine intelligence, enabling applications like adaptive autonomous agents and versatile shopping systems. However, modern object detectors are limited by the manual annotations of their training data, resulting in a vocabulary size significantly smaller than the vast array of objects encountered in reality. To overcome this, the open-vocabulary detection task (OVD) has emerged, utilizing image-text pairs for training and incorporating new category names at test time by associating them with the image content. By treating categories as text embeddings, open-vocabulary detectors can predict a wide range of unseen objects. Various techniqu…  ( 93 min )

I have an environment where there are multiple agents being represented by one neural network (so the policy outputs all of their actions). These actions as time goes on should not exceed a certain constraint level or they will put the environment into an undesired an irrecoverable state. I am wondering what the best way to inform these agents of this cumulative action constraint? I have appended it to my state vector but since the observation without this cumulative action is still a 625*1 vector, I think adding that constraint as just one additional state is causing it to be drowned out by the state size. Any ideas of how to addreess? submitted by /u/Feisty_Relation_2359 [link] [comments]  ( 9 min )

I have a multi agent competitive RL problem which I solved. Now, I want to show that all agent’s policies are at a nash equilibrium of the problem. How can I do this? Also, some things must be considered. First, I can’t mathematically model the environment so some how I have to numerically show that they reached nash eq. Another thing that I find is deviate the action of each agent and show that they don’t get a better reward but the problem is there is a actor network for each agent. How can I show deviation from the optimal policy? submitted by /u/Brief-Emotion6291 [link] [comments]  ( 9 min )

submitted by /u/x9182 [link] [comments]  ( 9 min )

I'm attempting to train an agent using A2C, where the agent generates a vector as its action at each time step. This vector represents a bidding curve, and a crucial property is that it must always increase monotonically. Otherwise, the bid is considered invalid. For example, [0, 1.2, 4.5, 58, 92.65, 104.3, 104.3] is valid because each number is greater than or equal to the previous one. I'm looking for guidance on how to design this setup, impose these constraints, and handle cases where the agent violates the sequence. While using negative rewards might not be effective due to the potential for generating numerous invalid bids, I'm unsure about the right approach. Could someone assist me with this? submitted by /u/uonliaquat [link] [comments]  ( 9 min )

Companies are discovering how accelerated computing can boost their bottom lines while making a positive impact on the planet. The NVIDIA RAPIDS Accelerator for Apache Spark, software that speeds data analytics, not only raises performance and lowers costs, it increases energy efficiency, too. That means it can help companies meet goals for net-zero emissions of Read article >  ( 6 min )

On July 31, 2023, a giraffe with no spots was born at Brights Zoo in Tennessee. She's a uniform brown with pretty white highlights around her face and belly, like a Jersey cow or a white-tailed deer. Image recognition algorithms are trained on a variety of images from  ( 5 min )

AI Weirdness: the strange side of machine learning  ( 2 min )